Keywords: grid computing, application monitoring, QoS, Vigne

Fournir de la QoS au travers d'un service de supervision d'applications pour grille

Résumé : Superviser des applications distribuées s'exécutant sur une grille de calcul est compliqué dans la mesure où ces applications s'exécutent sur plusieurs noeuds hétérogènes appartenant à différents domaines d'administration. Un service de supervision d'application doit fournir aux utilisateurs et aux administrateurs des données fiables sur les applications qui s'exécutent sur la grille. Nous présentons dans ce papier un service de supervision d'applications pour grille conçu pour traiter les problèmes de haute disponibilité et de passage à l'échelle. Ce service fournit des informations sur l'état des applications, sur les défaillances et sur les consommations de ressources. Un ensemble de mécanismes de supervision sont utilisés en fonction de la nature des noeuds de la grille pour superviser de manière efficace les applications. Les expériences menées sur la plate-forme d'expérimentation Grid'5000 montrent que le service fournit des informations fiables avec un coût minimal sur les performances de la grille.

Mots-clés : grid computing, supervision d'applications, qualité de service, Vigne

1 Introduction

Computational grids [START_REF] Foster | Computational grids. The grid: blueprint for a new computing infrastructure[END_REF] bring together a great number of computing resources possibly distributed on several administrative domains. They can provide the amount of resources needed for High Performance Computing (HPC). But executing and managing distributed applications on grids is a challenge since grid nodes are heterogeneous and volatile. Inherently to the grid nature, grid services must provide the user with dependable and consistent services to ease grid use.

In this paper, we present GAMoSe, a grid application monitoring service. The goal of this service is to facilitate the execution and the management of applications on grids by providing information on application execution. The architecture of GAMoSe has been designed to handle grid specificities, i.e. node volatility , node heterogeneity and grid scale, to provide quality of service (QoS) for users. QoS is also ensured by the accuracy of the monitoring mechanism that can provide dependable information on the state of the applications, resource utilization and failure detection. Furthermore GAMoSe is totally transparent for applications and for the operating system of grid nodes.

The rest of the paper is organized as follows. In Section 2, we present the context of our work. Section 3 exposes the requirements and the information GAMoSe should provide. The architecture and the communication model of the service are presented in Section 4. Section 5 describes the QoS provided for grid users. In Section 6, we present implementation details on monitoring mechanisms. GAMoSe has been integrated into the Vigne Grid Operating System [START_REF] Jeanvoine | Using overlay networks to build operating system services for large scale grids[END_REF][START_REF] Rilling | Towards a Self-Healing Grid Operating System[END_REF]. An evaluation of the service is presented in Section 7 and Section 8 describes related work. Finally, we draw conclusions from this work in Section 9.

Context

In this section, we expose the context of our work and the assumptions we made. We also define some terms we use in this paper.

Grid Model

Computional grids bring together a great number of computing resources distributed over many administrative domains. In this study, we consider grids with an uncountable number of nodes. A grid node can be a Linux PC or a cluster which is also seen as a single node from a grid point of view. A cluster can operate with a Single System Image (SSI) or with a batch scheduler. As the goal of an SSI is to give the illusion of a single SMP machine, we consider that a SSI cluster is equivalent to a Linux PC at grid level: we call them Linux nodes. A grid is a dynamic system in which nodes can join or leave at any time and where failures can occur. Failures can be node failure or failure of a communication link.

Each node of the grid has its own local Operating System (OS). To provide services at grid level, a Grid Operating System (GOS), Vigne for instance, is executed on each node on top of the local OS. A GOS provides services like resource discovery and allocation or job management. This paper focuses on an application monitoring service.

Application Model

A distributed application can make use of resources provided by the grid since it can run on several nodes. Our service targets this kind of application.

We call application components the different parts of a distributed application. An application component runs on a single grid node, as illustrated on Figure 1, and can be composed of one or many processes. These processes may be created dynamically during the execution. Several application components, which can belong to different applications, can be executed concurently on the same node.

Requirements

To have a global view of GAMoSe, we first detail the information it provides and study the usefulness of this information. In the second part of the section, we deal with the properties of the service. In a last part, we explain why we want the monitoring mechanisms to be transparent.

INRIA

Definition of the Service

The goal of a grid application monitoring service is to provide data on the execution of applications in the grid. The information provided by GAMoSe is the state of the application, information on failures and the among of resources utilized.

State of the Application Components

The best way to give information on the state of a distributed application is to give the state of each application component. GAMoSe does not try to compute a global state for the application since it would not be pertinent. For example, if the state of a distributed application is running, it could mean that all application components are waiting for resources to execute except one which is already running. It could also mean that all components have finished executing except one which is still running.

The state of an application component qualifies its evolution on the node it has been submitted to. However, GAMoSe cannot give the percentage of computation executed because this evaluation can only be done by instrumenting the application code and this does not meet the constraint of transparency exposed in Section 3.3. The state of the application components enable the user to keep an eye on the evolution of the applications she has submitted to the grid.

Failure Detection Determining the state of application components also includes failure detection. As we saw in Section 2.1, failures of grid nodes or communication links can occur. These failures need to be detected because it can induce an issue for the global application achievement if they affect nodes where application components were running. Application components can fail for many other reasons, a bug in the application code or the reception of a signal like SIGKILL for instance.

Providing information on failures is mainly useful for grid application management. Section 5.2 details how accurate information on application failures can be used to improve application management.

Resource Consumption

The resource consumed by an application during its execution must be evaluated. GAMoSe measures CPU time utilization and memory usage. Measuring resource consumption is useful for accounting. It can also help administrators to evaluate the level of use of their resources.

Service Properties

GAMoSe has to deal with the grid specificities presented in Section 2.1, to ensure a high and constant QoS.

High Availability: Grid nodes are unreliable due to disconnections and failures. GAMoSe should be highly available and provide accurate information despite reconfigurations.

Scalability: A multitude of applications can potentially run simultaneously on the grid. Monitoring all these applications will generate a large amount of data. Dealing with this amount of data should impact neither the service performance nor the grid performance.

Accuracy: On each node of the grid, the monitoring of the application components must be as precise as possible according to the specificities of the local OS. The failure of one process can induce the failure of an application component. That is why the application monitoring service has to monitor all application processes, even those created dynamically.

Constraint: Transparency

For several reasons, we have added a constraint to the monitoring mechanisms used: they should be transparent for the applications and for the local OS. First of all, GAMoSe must be transparent for the applications in order to work with legacy codes. It must support applications that were not initially designed to be monitored by the service. That is why it should require no modifications in the applications and no relinking.

Moreover GAMoSe must be transparent for the local OS since modifying an OS is a complex task that induces unwanted effects. First, resource owners will probably not share their resources on the grid if it has an impact on their operating system. Then, a resource in the grid can also be used by a local user outside the grid context. In this case, she probably does not care about grid features.

Design Principles

Requirements detailed in Section 3.2 have to be taken into account when designing GAMoSe. The architecture of the service and the communication model must provide support for scalability and high availability.

Architecture

Dealing with node failures is the most difficult case to ensure high availability of the service. Therefore, no node should have a central function to avoid a single point of failure issue that would compromise the entire service. Futhermore, GAMoSe must be able to manage efficiently large amount of data. Thus, a distributed architecture is the appropriate solution to overcome these issues.

The architecture of the service is presented in Figure 2. Two types of entity compose the service: application monitors and component monitors. In this example, two applications, each composed of two application components, are monitored. Component monitors are not represented on nodes hosting application monitors, even if one is present. Each application has its own application monitor. An application monitor is created when an application is submitted to the Grid OS and is destroyed after the end of the application execution. The application monitor collects the monitoring information about each component of the application and compute the application resource consumption. To collect information, application monitors have to interact with component monitors.

In the system, the nodes are organized in a structured overlay network. Thus, each node has a location independent name. Thanks to key based routing (KBR), it is possible to communicate with any node if its logical name is known. Furthermore, a distributed hash table (DHT) service is built on the top of the structured overlay. This DHT service has several channels, particularly an application monitor channel. As a consequence, an application monitor can be reached from anywhere in the network only by knowing its logical name. The logical name is obtained during the submission of the application, is unique and is independent of the application submission node. The overlay network features of the system are described in [START_REF] Jeanvoine | Using overlay networks to build operating system services for large scale grids[END_REF].

Component Monitor

There is a component monitor on each node of the grid. The function of the component monitor is to monitor all application components executed on the local node. So a component monitor is not dedicated to an application. The data collected by the component monitor are stored on the local node in a storage unit associated to the monitor. The storage is local to limit network communications because monitoring data are typically more frequently updated than read. Moreover, storing the data locally distributes the amount of monitoring data to store among all the nodes of the grid. The component monitor communicates with the application monitor through the communication model described in Section 4.2.

Communication Model

Network is a critical resource as components of distributed applications need to communicate with each other. Thus, the network bandwidth used by grid services must be minimal.

Notification To get information about a component, the application monitor could periodically send a request to the concerned qcomponent monitor. But this method induces useless communications if the information on a component has not changed since the last update. To reduce these useless communications, we introduce a communication model based on notifications. In this model, communications are initiated by the component monitors. When a component monitor detects an important change in the state of a component, it notifies this change to the application monitor and through the same message, it updates also the resource consumption of the component. The important changes that are notified are the beginning and the end of the execution and the application failures.

INRIA

Query/Response Query/response is the communication model used between users and application monitors. The user can send a request to the application monitor that monitors her application. The application monitor forwards this request to the concerned component monitors to get the updated information about resources consumption of each application component and then sends the response to the user.

5 How GAMoSe Provides Grid Users with QoS?

GAMoSe provides the grid system with the ability to harvest application executions. This section presents the benefits for the grid users to use GAMoSe.

High Availability of the Monitoring Service

The first step to provide users with a high QoS level is to ensure the high availability of the monitoring service. Thus, the service must be resilient to nodes failures induced by the dynamic behavior of the grid.

Nodes failures are handled by the application monitor (see Section 4.1.1). Two cases may happen.

First of all, a failure may occur on a node hosting an application component. The failure implies losses of information concerning resources utilization. Such losses are not an issue, if we assume that a grid user should not pay for the utilization of resources that sustained failures. Otherwise, the losses can be avoided by regularly sending utilization reports to the application monitor.

Then, a failure may occur on a node hosting the application monitor. Because the application monitor is a key component for GAMS, it must live through failures.

As stated in Section 4.1.1, the application monitor is based on a structured overlay network with a DHT. A mechanism of group communication built on the top of the structured overlay allows to send messages to a group of replicas. The messages sent to an application monitor are atomically multicast to the group of replicas of the application monitor. The application monitors are actively replicated. The consistency of the replicas is ensured since the application monitors rely on a deterministic automaton. The nodes hosting the replicas are automatically chosen using the DHT service. As a consequence, with the help of group communications and duplication facilities of the DHT, the application monitors is highly available. More details of the self-healing properties of the application monitor are presented in [START_REF] Rilling | Towards a Self-Healing Grid Operating System[END_REF].

QoS Provided to Grid Users

Reliable Application Execution

GAMoSe provides two mechanisms to execute applications reliably. Indeed, it handles coarse-grained failures that are failures of nodes and it handles fine-grained failures that are process failures.

In case of a node failure, the application monitor is able to detect it. Indeed, the application monitor has an heartbeat mechanism to check that the nodes used by the application components are alive. So, when the application monitor detects the failure of a node used by an application component, it applies a fault tolerance policy like restarting the application component on another node from scratch or from a checkpoint.

Several circumstances may lead to failures in application executions. Some issues are induced by errors in the application code so nothing can be done to achieve a correct execution. However, some issues are induced by the execution context. For instance, if an application uses a shared /tmp directory that becomes full, the application is killed if it tries to write a file. Thus, if this application is restarted to another node, its execution is likely to be correct.

The component monitor (see section 4.1.2) is able to deal with this kind of failure. Indeed, it monitors the execution of every application processes and detects any abnormal termination. Thus, on detecting an abnormal termination, the component monitor notifies the application monitor that will be able to apply a fault-tolerance policy as described above.

Ease Problem Diagnostics

In the event of an application failure, some issues depend on the execution context and some other depend on errors in the code. In this latter case, the component monitor is useful because it can detect the reason for the failure, for instance, the signal sent to the application or the error code. Then, the grid user can be informed of the reason for the failure and possibly fix the code of her application.

Accuratly Account the Resources Utilization

In production grids where resources are hired to consumers, the accuracy of the resource utilization measure is a necessary condition to fairness. In this way, the component monitor (see Section 4.1.2) provides fine-grained information about resource utilization. Indeed, by monitoring each process creation and termination, GAMoSe measures accuratly the CPU and memory resources used by an application. If Service Level Agreement (SLA) are used in the grid, those measurements can help services that have to control the respect of agreements [START_REF] Keller | Managing Dynamic Services: a Contract Based Approach to a Conceptual Architecture[END_REF].

According to the accounting policy, as stated in Section 5.1, information locally stored on a node may be lost or not. If utilization information must be kept, a tradeoff must be found between the frequency of sending local information to the application monitor and the bandwidth cost.

Implementation

We have implemented a prototype of GAMoSe and integrated it into Vigne. In this section, we first briefly present this prototype. Then we focus on monitoring of application INRIA components. We describe the monitoring mechanisms that we use according to the local OS.

Integration to Vigne

Vigne [START_REF] Jeanvoine | Using overlay networks to build operating system services for large scale grids[END_REF][START_REF] Rilling | Towards a Self-Healing Grid Operating System[END_REF] is a GOS for large scale grids with heterogeneous and unreliable nodes. Vigne is based on a distributed architecture providing self-healing capabilities.

Vigne is designed to provide a simplified view of a grid for users. Vigne provides services like resource allocation or application management. All these services are built on top of structured and unstructured peer-to-peer overlays. Vigne provides a simple interface for users to enable them to submit jobs and to get information on the job they have submitted.

We have integrated our service into Vigne so that users can get monitoring information on the applications through the user interface of Vigne. Thanks to the structured peerto-peer overlay and KBR, we have been able to easily locate application monitors and to establish communications between application monitors and component monitors.

Monitoring of Application Components

The information on application components must be as precise as enabled by the local system. In this section, we detail monitoring mechanisms used by the component monitors on clusters with batch schedulers and on Linux nodes.

With Batch Schedulers

An application component is submited as one job to a batch scheduler. Every batch scheduler provides information on jobs. They give the state of the jobs, including fail state, and most of them also give data on resource utilization. So, the only work for the component monitor is to retrieve the information provided by the batch scheduler. Due to DRMAA [START_REF]Distributed Resource Management Application API[END_REF], we can have the same interface for every batch scheduler and also get uniform information.

Many batch schedulers only monitor the first process of a job. So the monitoring can be inaccurate, especially for failure detection, if application components create new processes dynamically. However, we do not want to modify batch schedulers to improve monitoring because of the constraint of transparency expressed in Section 3.3.

On Linux Nodes

On Linux nodes, i.e. Linux PC or SSI clusters, the monitoring service can monitor processes. For each application component executing on a node, the component monitor of this node must know at any time the identity of each process belonging to an application component to be able to give accurate information on resource utilization or to detect failures. The problem is that, from an external point of view, creation and termination of processes are not governed by any rule. This means that new processes can be created or terminated at any time during the execution of the application component.

Using the process group ID of Unix systems can be a simple solution to identify every process of an application component but this solution is not reliable because programmers can change the process group ID of a process with the setpgid system call.

By intercepting the systems calls implied in process creation and termination, we can get the information we need for process monitoring:

fork is called to create a child process. Intercepting fork enables to know the new processes created.

exit terminates the current process. Intercepting exit enables to know the processes terminating.

waitpid waits for the state of a child process to change, i.e. the child process has terminated or the child process was stopped by a signal. Intercepting waitpid enables to know processes ending and how they end.

Using LD PRELOAD, we wrap the libc functions corresponding to these three system calls with monitoring code. This code transfers useful information to the component monitor when one of these system calls is used by an application component. We can see this mechanism as the generation of an event on the execution of these system calls. Thus the component monitor is aware of each dynamic process creation and can detect when and how processes terminate. The main limitation of this solution is that LD PRELOAD can not be used with statically linked applications. As LD PRELOAD is set just before executing an application component, our code is used only by the processes of the application component. So the parent process of the first process of an application component does not use the wrapped functions. fork and waitpid are not intercepted for the first process since they are called by its parent process: creation and failure of this process are not detected. To overcome this problem, we use a start process. A start process fork to execute an application component. LD PRELOAD is set before executing the start process so that fork and waitpid can be intercepted for the first process of the application component.

Evaluation

The main aspect we want to evaluate is the impact of GAMoSe on performances since we target HPC. Evaluations focus on Linux nodes since the role of the component monitor is very limited with batch schedulers. We first measure the impact of system calls wrapping on application execution time. Then we present an experience made on the Grid'5000 testbed to evaluate the cost of communications inside GAMoSe.

Influence of System Calls Interception on Execution Time

The wrapping of libc functions with LD PRELOAD used to intercept system calls can induce an overhead on the applications execution time. We evaluated this overhead. Our measure-INRIA ments were made on a workstation with a 3.6 GHz Pentium 4 processor and a 2.6.14 Linux system.

We executed an application which creates 100 processes. Each of these processes terminates with exit and is waited with waitpid. Each process simulates a computation time with a sleep of n seconds. We measured the execution time of the application with various values of n, with and without the wrapping of libc functions. The results are summarized in Table 1. The execution times are computed as the average over 20 executions. The execution with n=0 shows that the maximum overhead is 15,5%. But this is not a realistic case because our target is scientific applications. So we can assume that processes run longer and have to do some computation. The execution with n=100 is more realistic. In this case, the overhead is only 0.07%. We can conclude that the overhead induced by system calls interception is negligible.

Communication Cost

To evaluate the cost of communications between the entities of GAMoSe, we have executed Vigne on the Grid'5000 testbed. The Vigne daemon has been deployed on 116 nodes spread over 7 Grid'5000 sites: Bordeaux, Lille, Lyon, Orsay, Rennes, Sophia, Toulouse.

We have submitted 464 applications to Vigne and measured the amount of data sent by each high level services of Vigne through the network. Each application is composed of one application component. The results are summarized in Table 2. At the end of the experience, every application components have been executed. Beginning and termination of the components as well as their resource consumption have been notified to the application monitors. In this experience, only the executable of each application component has been transferred, about 12 Ko per application component. This experience shows that the percentage of network bandwidth used by GAMoSe is very small in comparison with the other high level services of Vigne. Furthermore, the total amount of data sent by GAMoSe is small enough to conclude that the cost of the communications inside GAMoSe is very limited.

Related Work

The survey [START_REF] Zanikolas | A taxonomy of grid monitoring systems[END_REF] presents an overview of grid monitoring systems. It identifies scalability as the main requirement monitoring systems. That is why most services are based on a distributed architecture. As our service is dedicated to application monitoring, we have created application monitors which enable to merge the data on a per application basis. This cannot be done in a generic monitoring architecture like the Grid Monitoring Architecture (GMA) [START_REF] Tierney | A Grid Monitoring Architecture[END_REF] for instance. In this case, the user has to develop her own mechanism to aggregate information. Three models of interactions between producers, entities that produce monitoring data like the component monitors, and consumers, entities that receive monitoring data like the application monitors, are supported by GMA: publish/subscribe, query/response and notification. We have used two of these communication models: notification and query/response. Thus the network bandwidth we use for data collection is optimized in comparison with the periodical poll used in Ganglia [START_REF] Massie | Ganglia Distributed Monitoring System: Design, Implementation, and Experience[END_REF] or in the XtremWeb grid middleware [START_REF] Cappello | Computing on large-scale distributed systems: Xtrem Web architecture, programming models, security, tests and convergence with grid[END_REF].

In the field of QoS, monitoring mechanisms used in existing grid systems to monitor applications executed on Linux nodes are not accurate or not transparent. The transparent solution which is mainly used is to scan the /proc pseudo file system to get information on the first process created when executing the application. End of the application is detected when the process disappears from /proc. Although this solution does not allow to monitor processes created dynamically and to detect failures, it is the solution used by the Globus Grid Resource Allocation and Management protocol (GRAM) [START_REF] Czajkowski | A Resource Management Architecture for Metacomputing Systems[END_REF], on which Condor-G [START_REF] Foster | Condor-G: A Computation Management Agent for Multi-Institutional Grids[END_REF] is based, by Nimrod/G [START_REF] Buyya | Nimrod/G: An Architecture for a Resource Management and Scheduling System in a Global Computational Grid[END_REF], by XtremWeb and by Legion [START_REF] Grimshaw | The Legion Team. The Legion vision of a worldwide virtual computer[END_REF]. The OCM-G [START_REF] Balis | Monitoring Grid Applications with Grid-Enabled OMIS Monitor[END_REF] monitoring system or Autopilot [START_REF] Vetter | Real-Time Performance Monitoring, Adaptive Control, and Interactive Steering of Computational Grids[END_REF] enable to accurately monitor applications in grid systems but, in contrast with our approach, they are not transparent for applications since the application code needs to be instrumented.

Conclusion

We have described in this paper GAMoSe, a grid application monitoring service. This highly decentralized service has been designed to scale with the number of applications executed in a large grid and with the number of processes and components in an application. GAMoSe INRIA tolerates node joins and leaves and is highly available despite node or communication link failures. GAMoSe provides dependable information on application, i.e. on failure and resource utilization, especially on Linux nodes where it is able to monitor every process of an application component even dynamically created processes. In contrast with other monitoring systems described in the literature, the accurate monitoringh mechanisms of GAMoSe are completelly transparent for the applications.

Due to the accurate information it supplies, GAMoSe provides QoS to users. It enables to execute applications reliably since node and process failures are detected. In the event of a process failure, GAMoSe reports the cause of the terminaison. The user can thus diagnose the reason for the failure and know if the error comes from the application code for instance. Finally, providing accurate information on resource utilization is very important if grid resources are billed to users.

GAMoSe cannot accurately monitor statically linked applications. It would be possible to monitor those applications if system calls were intercepted at system level. This issue will be further investigated in the framework of the XtreemOS European project [START_REF]The XtreemOS Project[END_REF] targeting the design and implementation of a Linux-based Grid operating system with native support for virtual organizations.

Figure 1 :

 1 Figure 1: A distributed application executing in a grid environment

Figure 2 :

 2 Figure 2: Architecture of the monitoring service

Table 1 :

 1 Overhead induced by system calls interception

		Standard	Execution time
	n	execution	with	Overhead
		time	LD PRELOAD
	0 s	0.413 s	0.477 s	0.064 s (15.5%)
	1 s	1.416 s	1.497 s	0.081 s (5.72%)
	10 s	10.418 s	10.483 s	0.065 s (0.62%)
	100 s 100.415 s	100.482 s	0.067 s (0.07%)

Table 2 :

 2 Network bandwidth usage of Vigne high-level services

RR n 6070

Ropars & Jeanvoine & Morin


Farid Khoucha

Mohamed Benbouzid
email: mohamed.benbouzid@univ-brest.fr

Yassine Amirat

Abdelaziz Kheloui

Integrated Energy Management of a Plug-in Electric Vehicle in Residential Distribution Systems with Renewables

Keywords: Vehicle-to-grid (V2G), vehicle-to-home (V2H), residential distribution, smart house, balance supply and demand

come

Integrated energy management of a plug-in electric

vehicle in residential distribution systems with renewables

I. INTRODUCTION

To deal with the global energy crisis and environmental pollution, it is becoming necessary to integrate renewable energy generation such as solar power, wind power and hydroelectric Power into the existing power grid. Meanwhile, hybrid electric vehicles (HEVs) and electric vehicles (EVs), particularly the plug-in electric vehicles (PEVs), for green transportation have attracted increasing attention [START_REF] Chan | Electric, hybrid, and fuel cell vehicles: architectures and modeling[END_REF], [START_REF] Khaligh | Battery, ultracapacitor, fuel cell and hybrid energy storage systems for electric, hybrid electric, fuel cell and Plug-In hybrid electric vehicles[END_REF]. Because of the intermittent nature of solar power, large-scale integration of solar power poses a challenge on the power grid in both transient and steady states. Vehicle-to-grid (V2G) operation can help address the challenges by acting as a mobile energy storage device [START_REF] Liu | Opportunities and challenges of vehicle-to-home, vehicle-to-vehicle and vehicle-to-grid technologies[END_REF]. A large number of EVs were considered adjustable generators, which were used to provide power to minimize both cost and emission. However, the characteristics of the V2G power and the availability of EVs were not adequately considered in the V2G optimization system. Then, the study and analysis of the integration of an electric vehicle into the micro-grid energy management was performed, but unfortunately faced with the limitations imposed on the V2G power due to the characteristics of EV batteries [START_REF] Lunza | Review of the impact of vehicle-to-grid technologies on distribution systems and utility interfaces[END_REF].

Moreover, several techniques have been proposed and implemented to ensure optimal energy transfer in the V2G system to maximize the benefit of V2G [START_REF] Rotering | Optimal charge control of plug-in hybrid electric vehicles in deregulated electricity markets[END_REF]. However, there were shortcomings that the power flow of EVs was unidirectional, the charging rate was fixed at the maximum limit, and the optimal solution was the selected charging intervals during the plug-in period.

With the ever-increasing popularity of EVs [START_REF] Han | Estimation of achievable power capacity from plug-in electric vehicles for V2G frequency regulation: Case studies for market participation[END_REF] the frequency regulation provided by V2G operation has been actively studied. Several studies have focused using electric vehicles of different sizes and configurations for the control of the frequency and voltage fluctuations on the grid with connected V2G system [START_REF] Han | Development of an optimal vehicle-to grid aggregator for frequency regulation[END_REF]. Moreover, EVs could provide various auxiliary services such as the energy scheduling for load leveling [START_REF] Kim | Bidirectional energy trading and residential load scheduling with electric vehicles in the smart grid[END_REF], the minimization of the ratio between time and charging cost [START_REF] Ding | Optimal PHEVs charging management by tradeoff between utility cost and user satisfaction[END_REF], and the spinning reserve [START_REF] Viswanathan | Second use of transportation batteries: Maximizing the value of batteries for transportation and grid services[END_REF]. Although a variety of regulation services are suggested for V2G energy management, they are still far from being unique and optimal solution.

This paper deal with the development of a wide control structure for an advanced smart house connected to a microgrid, which includes renewable energy sources, battery and an EV, so that the V2G power can be properly deployed. The scale of distributed generators and the EV penetration degree are assumed for illustration. Nevertheless, they can readily be modified and adapted for other case studies.

II. SYSTEM CONFIGURATION

The residential micro-grid or Smart House (SH) considered for this work corresponds to one household and it is described in Fig. 1. The distributed energy resources considered are a micro-turbine, an active generator (photovoltaic module associated with battery), and the battery of a PEV. At this home, the home energy management system (HEMS) communicates with the different devices constituting the load and all the energy sources by smart meters. Understanding each device condition and information on changes in power consumption, control of different devices, and energy management become easier using smart meters and sensors,.

The SH has photovoltaic (PV) system as a renewable energy source (this source is very effective in reducing energy consumption especially in Algeria with many isolated areas). This hybrid generator system is also used to perform standalone operation when a perturbation affects the electrical network. The power generation system of the SH is composed by the PV system, medium-capacity battery and plug-in electrical vehicle (PEV). The use of medium-capacity battery can be expected to reduce the cost and installation space as compared to the use of high capacity battery. A standard smart house is required a battery capacity of 8kWh to ensure the necessary power at home per day during the blackout.

The configuration of the simulation model is shown in Fig. 2. The PV system is connected to the DC-DC converter, which can perform the MPPT control. Similarly, the battery is connected to the DC-DC converter, which can perform the DC link voltage control around 400V. The EV is also connected to the DC-bus by a DC-DC converter. This adopted connection scheme allows reducing power conversion stages and therefore leads to an efficient energy management. The inverter converting DC power sources to AC power are connected to single-phase utility of 220V and 50Hz.

III. EV HOME CHARGING PROCESS

An electric vehicle in V2G process is different from other devices used for housing power regulation service, peak shaving and valley filling mainly for its dependence on batteries. Battery life is very influenced by the number of cycles, depth of discharged, charge/discharge rates and other factors [START_REF] Benrabah | FC/battery power management for electric vehicle based interleaved DC-DC boost converter topology[END_REF][START_REF] Wang | Grid power peak shaving and valley filling using vehicle to grid systems[END_REF]. Users of electric vehicles usually require the batteries to be fully charged in the shortest period of time as possible to increase the vehicle's autonomy. Both battery life and charging time are therefore major constraints for electric vehicle plugged to the grid. The maximum time for recharging a battery should be set as a tradeoff between the need of having a quick recharge as required by the user, and participating in power regulation service as desired by the energy manager for the connected home to a smart micro-grid. One could envisage incentive schemes that encourage EV users to allow the participation of their vehicles in the V2G system with the two configurations Vehicle to home (V2H) and home to vehicle (H2V). The V2G system could also include a prioritizing system that considers the history of the battery usage when the vehicle is disconnected to set an appropriate charging time.

In addition to battery and consumer constraints, the power regulation service like peak shaving and valley-filling V2G system needs to meet the following constraints.

A. Constraints on the Power of EVs Involved in the V2G System

( ) ( )

⎪ ⎪ ⎩ ⎪ ⎪ ⎨ ⎧ - ≤ ≤ ≤ t x t z y b b y a a y i i i i i i i i i 0 0   (1)
Where a i is a positive number equal to the maximum value of the total charge power from the micro-grid to the plugged EVs, and is b i is a negative value equal to the maximum value of the total discharge power from the EV connected to the micro-grid. x i (t) is the forecast load value, determined from historical power usage and z i (t) is the target load value, derived from the states of the load and vehicle.

This constraint limits the value of y i (t) to be less than the maximum service capability of EVs, depending on the vehicle ability to overcome the requirement of the traffic, and the power of the load in the area of vehicle use. However, the value of y i (t) cannot be more than the supply and demand of the grid.

B. Constraints on the Vehicle Battery during Charging and Discharging Modes

⎪ ⎪ ⎩ ⎪ ⎪ ⎨ ⎧ × ≤ ≤ × - ≤ ≤ ≤ ≤ dis i d i ch i c d dis i c ch i V I P V I C C C C max max max max 0 0 (2)
Where

ch i C is the charge ratio, dis i C is the discharge ratio, max c C is the available maximum charge ratio, max d C is the available maximum discharge ratio, ch i V is the charge voltage, dis i V is the discharge voltage,

C. Constraints on the EV Battery Capacity

( ) ( ) ( ) ⎪ ⎪ ⎩ ⎪ ⎪ ⎨ ⎧ - ≤ Δ ≤ - ≤ Δ ≤ - = Δ e i dis i e i ch i e Q SOC SOC Q Q SOC SOC Q Q SOC SOC Q i min max min max max 0 0 (3)
Where i SOC is the real-time capacity state, max SOC and min SOC are the maximum and minimum value of SOC, respectively.

max i Q Δ , ch i Q Δ , dis i Q Δ
and e Q are the maximum available capacity, the available charge capacity, the available discharge capacity and the rated capacity of the EV battery, respectively.

To ensure the battery life and energy efficiency, the range of SOC is limited. The maximum available capacity is decided by the rated capacity and the difference between, max SOC and min SOC .

EV user-setting constraints are also important factors that affect the capability of the EV to participate in V2G system to power supply and demand regulation. For transportation, EV should satisfy the driving demand first, which means that the EV user may set the upper and lower limits of SOC and the special period in which the EV can be involved in V2G system, in this case, EV even if plugged, the battery can only be charged for the planned trip by the user (H2V mode). The EV user set parameters shown in Fig. 3 SOC , the EV operating state represented in Fig. 3 is decided by the energy management system according to the home power and user requirements.

IV. HOUSE ENERGY MANAGEMENT SYSTEMS

In this paper only, a single house or prosumer and a gas micro-turbine are considered in this micro-grid (Fig. 1). The House Energy Management (HEM) integrates three functions: a load manager, an advanced meter, and a local energy management. The load manager allows customers automatically planning the use of home devices (enable and disable) on the basis of low price, critical schedules and depending on the profile of the total consumption, such as uninterrupted supply of critical loads, time programmable use, etc. Moreover, it can reduce a part of the home power demand when the grid is under stress by disconnecting the accessible controllable loads [START_REF] Boynueqri | Energy management algorithm for smart home with renewable energy sources[END_REF]. An advanced meter feeds the local HEM as well as the load manager. Moreover, the utility is able to ping the meter. Photovoltaic panels are associated with a storage system which includes a set of batteries as a long-term storage device [START_REF] Kanchev | Energy management and operational planning of a microgrid with a PV-based active generator for smart grid applications[END_REF] and a battery of V2G as a dynamic regulator of power when the vehicle when is plugged-in and the state of charge of the battery allows it (Fig. 4). The interesting aspect of this hybrid generator is that it is able to deliver a prescribed power level (P HP_ref ) like a conventional generator (for example, a gas micro-turbine). The local energy management thus allows the use of PV energy according to the grid operator requirement and also at times when the sun is not shining.

In this case, the home batteries are required to provide the necessary power. This concept is called an active generator to make a difference with a conventional generator based only photovoltaic panels. Excess PV energy is stored in home batteries and EV Battery for use when needed, knowing that the vehicle is rarely plug-in to the home during the day. The HEM gives an intelligent control to facility to the grid operators and to allow faster adjustments to conditions and gives more flexibility to reroute power in a certain offered margin. Photovoltaic panels provide electrical power only during the day with a power peak around the midday. Meanwhile, vast production variations may occur.

According to the weather forecasting and the historic database of PV power, an ahead-approximated PV power prediction profile ( PV P ˆ) can be used. The load forecasting is also ahead-approximated the energy management. Based on historic electrical power production demands, the behavior of the loads can be forecast and estimated. Several factors influence the load in the electrical network: the weather situation (temperature, cloud coverage, etc.), economic activity (huge modifications of load forecasting are necessary during the holiday periods). An approximate load forecasting profile ( )

Load P ˆ
is given in Fig. 5. In this studied case, three power sources are considered: a PV-based active generator, a V2G system and a gas microturbine. Because of the renewable energy benefits (less gas emission and low operating cost), the PV-based active generator is considered as the main source, the gas microturbine and EV Battery are considered as a backup sources for the missing energy [START_REF] Yilmaz | Review of the impact of vehicle-to-grid technologies on distribution systems and utility interfaces[END_REF]. The objective is to set a minimum charging/discharging cycle for batteries every day. The depth of the discharge is maintained between 10% and 80% during normal operation to increase the battery lifetime. Here, the storage and EV battery capacity are 8 kWh and 5 kWh, respectively.

According to daily predictions of the available power, energy from the active generator, the EV battery energy when electric vehicle is plugged-in and the required power and energy of the loads (P Home ), a power production planning for the prosumer (P HP ) and for the micro-turbine (P Grid ) must be determined. As no power is available from PV panels during the night and the electric vehicle usually disconnected during the day, power references can be calculated separately for the night and for the day. In the day and when the EV is disconnected (Fig. 6), two cases are considered.

If the available PV energy added with the minimum gas turbine energy is less than the demanded load energy ( )

Home Grid PV E E E min _ < +
, the PV panels can work with a maximum power point tracking (MPPT) algorithm, and all PV power is injected in the grid. The gas micro-turbine has to generate the missing power.

Otherwise, the available PV energy added to the minimum gas turbine energy is more than the demanded load energy ( )

Home Grid PV E E E min _ > +
. Priority is then given to the renewable energy for the electrical production so that the gas micro-turbine works with minimum power and the active generator power is limited to the missing power. The energy management during the night depends on the available energy from batteries in homes and V2G battery (Fig. 7). This energy can be estimated or communicated by the HEM. Two cases are also distinguished. For both cases, the batteries in home have to be discharged in order to be ready for charging the next day and the EV battery must be fully charged for the trip planned by the user.

The three cases for the night operation are related to the EV connection as follow:

During heavy energy demands and if the available stored energy in both home and EV battery added to the minimum gas turbine energy is more than the demanded energy from the loads, priority is given to the active generator for the electrical production since it has enough previously stored energy from PV panels.

The gas turbine will work with minimum power and the EV is operating in V2H: Otherwise, the stored battery energy added with the minimum gas turbine energy is less than the demanded energy from the loads. Then, the power reference of the active generator is calculated in order to discharge the home batteries, and the gas turbine must generate the missing power and power required to charge the EV battery under the constraints imposed in section IV. In this case the EV is operating in H2V mode.

V. CASE STUDY SIMULATION RESULTS This section describes an example of a 1-day simulation. The vehicle is assumed to be pure electric vehicle and the EV driver leaves home at 6.00 am to go to the work (case of Algiers city in Algeria) and finishes at 7.00 pm without charging during working time. This simulation shows a 24 hours pattern.

According to the objective of the intelligent house energy management that is applied to this scenario, as it is shown in Fig. 9, vehicle battery is charged at the low-load hours to flatten the load profile curve (Fig. 10), this charging is also decided if the available PV power in the MPPT is higher than the requested power reference and if batteries are not full .The state of charge has to be estimated and compared with the maximum value.

Furthermore, at peak hours, vehicle battery starts to discharge to reduce the peak value of loads. The uncertainty of photovoltaic system is compensated by the micro-turbine in the day and replaced by the home battery in the night (Fig. 11).

VI. CONCLUSION

The energy management of houses and stand-alone and consumers and/or sites connected has strong relevance in the future power systems, mainly in micro-grids and smart grids operation context. This paper focuses on energy management methodology in real-time to obtain new solutions for the domestic consumers with plugged-in electric vehicle (V2G), in order to contribute with new approaches for the power systems. It is important to identify, within domestic consumers, the influence that load has in power consumption, and how the management methodology influences the total consumption of the domestic consumer taking into account the grid, house conditions, weather and the requirements of the EV users. In this paper a dynamic energy management was proposed to meet house demand within according to the different dynamics and nature of sources.

Simulation have been carried-out for a typical smart home daily consumption profile with two power peaks around the midday when the vehicle is disconnected and at 9.00 pm. The total energy required by the load and to recharge the home battery was mostly carried by solar panels (MPPT). The energy difference was supplied by the micro-turbine, so the peak power consumption has been avoided from the grid. The home battery was charged after the peak power and any extra energy was used during this peak power. To complete the control strategy a function related to the state of charge of the EV battery has been implemented. To limit both home and EV batteries degradation, charging and discharging cycles were limited by using a minimum and maximum charge limits. This power management strategy could also be applied when EV is not available, which means V2H would not be considered when EV is not at home. Based on the achieved simulation results, it is obvious that an energy management scheme with V2G would reduce the peak energy consumption with better management of batteries state of charge.

Fig. 1 .

 1 Fig. 1. Smart home scheme with V2G system.

  max c I is the charge current within the maximum charge ratio max c C , max d I is the current within the maximum discharge ratio max d C , and P i is the EV available power. The battery limits the current and power of each connected EV. The maximum charge and discharge current cannot be more than the charge current max c I and the discharge current max d I. Besides the current, the power limit relies on the voltage of the EV battery under this charge and discharge current.

Fig. 3 .

 3 Fig. 3. Battery charging and discharging strategy.

Fig. 4 .

 4 Fig. 4. Energy management of the V2G control system.

Fig. 5 .

 5 Fig. 5. A day household consumption prediction.

Fig. 6 .

 6 Fig.6. Power distribution when PEV is not connected to home.

Fig. 7 Fig. 8 .

 78 Fig. 7.

  period of reduced consumption, the EV is operating in H2V mode and the EV battery is added to different house loads:

Fig. 9 .

 9 Fig. 9. Energy management according to the power planning in the intelligent house.

Fig. 10 .

 10 Fig. 10. Daily EV battery SOC variation.

Fig. 11 .

 11 Fig. 11. Daily home battery SOC variation.

  can be represented as follows:SOC are the lower and upper limits of SOC indicated by the manufacturer or set by the user, respectively. To ensure the safety and increase the life of the EV battery, SOC . When the EV is plugged to the grid, the state of EV is decided by these constraints. If

	* min SOC	≤	* i ≤ SOC	* max SOC	(4)
	Where	* min SOC and	* max
				* min SOC cannot be lower than	min SOC , and also
	* max SOC cannot exceed
	i SOC is lower than	* min SOC , the EV needs to be preferably in
	the charge state (home to vehicle mode). If	i SOC is higher
	than	* max SOC , then the EV can enter into the discharge state
	(vehicle to home mode).
	If

max i SOC is between * min SOC and * max


E Ferdjallah-Kherkhachi
email: esseddik.ferdjallah-kherkhachi@univ-nantes.fr

E Schaeffer
email: emmanuel.schaeffer@univ-nantes.fr

L Loron
email: luc.loron@univ-nantes.fr

M E H Benbouzid
email: mohamed.benbouzid@univ-brest.fr

Effect of the Short-Term Temperature Changes on Diagnostic Indicator In Online Insulation Monitoring by Parametric Identification

Keywords: ¡ Condition monitoring, Fault diagnosis, insulation, parameter estimation, sensitivity analysis. I

Electrical generators of offshore wind or tidal current turbines are exposed to harsh marine and operating conditions. Predictive maintenance is therefore a key issue for the competitiveness of these energy generation systems. Generally speaking, the predictive maintenance is based on the monitoring of a Diagnostic Indicator (DI): the interpretation of its value or drift is used for the optimal planning of the corrective maintenance. In this work, we present briefly our new online monitoring technique of electrical machine winding insulation. This model-based approach consists in monitoring the drift of a DI built from the in-situ estimation of high-frequency electrical model parameters. The involved model structures are derived from the RLC network modeling of the winding insulation, with more or less lumped parameters. In the second part of the work, we investigate the effects of temperature changes on the estimated parameters of diagnostic models. A 1.5 kW low power wound stator is exposed to different temperature levels, from 30°C to 160°C, and for each temperature a series of experimental acquisitions is realized. Identification results show that resistance and inductance of a simple HF model structure are almost independent of temperature changes, while insulation capacitance increases with temperature increases: at 160°C it is 8% higher than its initial value at room temperature.

INTRODUCTION

The recent progress and policy decisions in Marine Renewable Energies (MRE) indicate that offshore wind and tidal current technologies are now mature enough for their industrial deployment over the coming years [1]. However, the harsh marine conditions, such as salty environment, water infiltration and thermal cycling, have brought serious challenges to their operation [START_REF] Judendorfer | Challenges to machine windings used in electrical generators in wave and tidal power plants[END_REF]. For high power electrical generators used in the MRE turbines, the stator rewinding is, without any doubt, the most expensive repair that MRE farm operators will deal with during life service [START_REF] Stone | Electrical insulation for rotating machines[END_REF]. To avoid the premature degradation of the insulation system that could lead to an unscheduled costly outage, the most efficient way is to continuously monitor the insulation health state [START_REF] Younsi | Online capacitance and dissipation factor monitoring of AC motor stator insulation[END_REF]. This is the guiding idea of the proposed diagnosis method based on the in-situ estimation of electrical parametric models derived from the RLC network modeling of the winding insulation system [START_REF] Liu | High Frequency Modelling of Stator Windings Dedicated to Machine Insulation Diagnosis by Parametric Identification[END_REF]. A Diagnostic Indicator (DI) built with the turn-to-turn or turn-to-ground capacitance estimated values can be used for evaluating the health state of the insulation system (see Fig. 1) [START_REF] Ferdjallah-Kherkhachi | Online Monitoring of Marine Turbine Insulation Condition Based on High Frequency Models[END_REF]; indeed it has been shown that the value of these capacitances increases with aging [START_REF] Younsi | Online capacitance and dissipation factor monitoring of AC motor stator insulation[END_REF], [START_REF] Savin | Turn-to-turn capacitance variations correlated to PDIV for AC motors monitoring[END_REF], [START_REF] Werynski | On-line monitoring of Insulation Aging in AC Motors Fed by PWM Inverters[END_REF]. Unfortunately, consequences of insulation aging, moisture, temperature or skin effect can be mixed in parameter estimated values of too lumped parameter models. Actually, a short-term temperature can affect the high frequency behavior of the insulation, and consequently the DI may depend on the temperature of service. In this work, we investigate the influence of winding short-term temperature changes on estimated parameters of several simple model structures derived from the high frequency input/output behavior of the insulation system. The underlying purpose is to evaluate this dependence and to propose, in further works, model structures that could take into account the difference between insulation aging and temperature variations due to service condition changes. Such structures should present the best tradeoff between simplicity for their identifiability in operating conditions and complexity for their diagnosis ability. The principle of the in-situ identification of the insulation system is presented in the next section. A short review of the effects of physical characteristics (temperature, frequency and moisture) on the insulation high frequency behavior is proposed in the third section. The last section presents experimental results and discussions.

II. IDENTIFICATION OF THE INSULATION SYSTEM FROM EXPERIMENTAL DATA

A. In Situ Identification of High Frequency Model Structures Fig. 2 shows the technical solution carried out for the in-situ excitation of the insulation system. A pulse generator applies a high frequency and high voltage excitation to the insulation system, between phases and ground wall. The capacitive coupling box contains adaptation and measurement resistances, and the impedance of the coupling capacitances can be neglected in the high frequency range of the input/output signals used for the insulation system identification.

B. Complexity of the Model Structures

The main concept of this paper deals with the system modeling and parameter identification of diagnosis model structures. For the insulation system, model structures can deal with very simple R-L-C circuits lumped in series or more complex RLC networks that can well explain the propagation of the voltage surges in the whole insulation system [START_REF] Liu | High Frequency Modelling of Stator Windings Dedicated to Machine Insulation Diagnosis by Parametric Identification[END_REF]. The increase of the structure fineness (i.e. the decrease of the size of the elementary physical volumes modeled by lumped R, L, C elements) tends to improve the physical signification of it parameters, and therefore to increase its diagnosis ability [START_REF] Liu | High Frequency Modelling of Stator Windings Dedicated to Machine Insulation Diagnosis by Parametric Identification[END_REF]. Furthermore, the more we try to take into account physical phenomena (such as non-linarites or skin effects), the more the structure requires parameters. Finally, ¢ £ ¤ ¥ ¥ ¦ § ¦ ¨¢ ¤ © ¥ ¨ model must present the best tradeoff between the structure fineness for its diagnosis ability and the number of parameters for its identifiability in operating conditions.

C. Continuous-Time Model Identification by the Output Error

Method and the Sensitivity Functions 1) General Principle Fig. 3 resumes the principle of the output error method with an output error model [START_REF] Trigeassou | Parameter estimation for -00782890, version 1, Control Methods for Electrical Machines[END_REF]: the system output y s (t) is considered as the sum of the model output for the £ right § value of the parameter and an output noise which embeds measurement and modeling noise. Let be an estimation of . Then, a simulation of the system output using only the measured input signal can be obtained by a numerical integration of the continuous-time state-space model defined by:

(1)

where is the parameter vector, is the state-vector, the input u(t) and the model output can be scalar or vector, and the functions f and g are based on physical laws which are generally non-linear with respect to parameters. Bold letters refer to vectors. This general form allows to study linear as well as non-linear model structure (with respect to input), with scalar or vector input and output. Then, the optimal estimated parameter vector is obtained by the minimization of the following quadratic criterion , [START_REF] Trigeassou | Parameter estimation for -00782890, version 1, Control Methods for Electrical Machines[END_REF]: where is the output error at sample time .

2) Non-Linear Optimization of the Quadratic Criterion

Near the optimum, and the second order Taylor expansion of the state-distance gives:

where and denote the gradient and the hessian of D at point . The gradient cancels at the critical point , and thus the derivation of (4) with respect to gives: Multiplying (5) by the inverse of the hessian matrix should allow reaching the objective point in one iteration from a starting point. Unfortunately, ( 4) is an approximation of the hyper-surface near the objective point, and therefore multiple iterations are required. One can for example use the Gauss-Newton algorithm [START_REF] Bachir | Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines[END_REF]: for which the gradient vector ) gives the direction of the search in the parametric space, whereas the inverse of the hessian matrix gives the depth of the descent. The calculation of and by numerical derivation would induce dramatic computational problems. A better solution consists in using the sensitivity functions. Indeed, the first and second-order derivative of (2) with respect to gives the following gradient and hessian expressions, which only depend on the measurements, the model simulated output and the values of the output-sensitivity functions [START_REF] Trigeassou | Parameter estimation for -00782890, version 1, Control Methods for Electrical Machines[END_REF]: where is the Jacobian matrix of .and is the output-sensibility function. The detailed methodology of Output error method for parameter identification and uncertainty study in the insulation monitoring context is presented in a previous work [START_REF] Ferdjallah-Kherkhachi | Online Monitoring of Marine Turbine Insulation Condition Based on High Frequency Models[END_REF].

III. A SHORT REVIEW OF HIGH FREQUENCY ELECTRICAL BEHAVIOR OF THE WINDING INSULATION SYSTEM

A. Effect of Insulation Aging

Winding insulation aging has been largely studied since the apparition of rotating machines [START_REF] Stone | Electrical insulation for rotating machines[END_REF]. In the context of online monitoring, recent researches have established a strong relation between insulation aging and the high frequency electrical behavior of insulation system. In [START_REF] Perisse | Robust diagnostics of stator insulation based on high frequency resonances measurements[END_REF], the authors showed that the high frequency resonances of a winding insulation change with aging: its variation is correlated with the increase of turn to turn capacitance of insulation. S. B. Lee et al [START_REF] Lee | An online groundwall and phase-to-phase insulation quality assessment technique for AC-machine stator windings[END_REF] have studied the inter-phases leakage current to evaluate the insulation-condition indicators for simplified insulation model, then a correlation of inter-turn and turn to ground capacitances with aging has been established.

B. Effect of Moisture

Humidity affects the dielectric response in two different ways: first, by penetrating inside the bulk of the insulation, and second by forming a conductive layer of moisture on the insulation surface. Farahani et al investigated in [START_REF] Farahani | Dielectric response studies on insulating system of high voltage rotating machines[END_REF] the effects of water content and aging on insulation system response in the frequency domain. They showed that their influence on the capacitance differs in the low and high frequency ranges. Humidity affects more the low frequency behavior, while aging effects are more remarkable in high frequency ranges.

C. Effect of Frequency

Frequency dependence phenomena such as the skin and proximity effects on winding electrical parameters are well known, especially in the low frequency range (< 10 kHz) In [START_REF] Venegas | A Finite Element Approach for the Calculation of Electrical Machine Parameters at High Frequencies[END_REF], a finite element model for the calculation of resistance, inductance and capacitance in high voltage machine winding is developed. Comparison between simulated and experimental measurements showed that capacitance is almost independent of the frequency over 100 kHz. The same conclusion is given in [START_REF] Farahani | Dielectric response studies on insulating system of high voltage rotating machines[END_REF] [START_REF] Guardado | A machine winding model for switching transient studies using network synthesis[END_REF]. However, the resistance is found strongly dependent on the frequency. It keeps increasing with the frequency increase in all frequency ranges.

D. Effect of Temperature

The effect of temperature on dielectric response of stator winding insulation is quite different, depending on the frequency ranges. R. Soltani et al [START_REF] Soltani | Temperature Effect on Dielectric Characteristics of Large Rotating Machines Insulation[END_REF] showed that the capacitance and dissipation factor of insulation materials are more sensitive to temperature variation at very low frequencies (0.1 Hz). However, in relatively high frequency (10 kHz) it is shown in this study and others [START_REF] Kimura | Multistress aging of machine insulation systems[END_REF] that the insulation characteristics change, and the degradation rate of the stator winding insulation increases with the temperature swing of the load cycles. Another investigation on the short-term behavior of dielectric response [START_REF] Younsi | Online capacitance and dissipation factor monitoring of AC motor stator insulation[END_REF] shows that the insulation capacitance and the loss angle first present a fast increase and stabilize after about one hour of heating: this is due to a quick thermal activated insulation conductivity and a slower permittivity. This short review shows the complexity of dielectric phenomena in the insulation system depending on temperature

IV. EXPERIMENTAL STUDY

A. Test Bench Fig. 4 presents the laboratory bench. The insulation system under test consists in a 1.5 kW star connected machine. The input voltage provided from the pulse generator and the output current charging/discharging the insulation system are acquired by an oscilloscope Yokogawa DL9140, equipped with a 8 bits AD converter rating at 2.5 GHz. Series of ten input/output records are performed at different temperatures, from 30°C±1°C to 160°C ±1°C, with 10°C gap. Experimental data are then used to compare the diagnosis potentiality of different model structures. Measurements had been carried outside the heat oven because its walls behave as a Faraday cage and disturb the excitation signal in the high frequency range.

B. Results and Discussions

In this study, several behavior model structures had been investigated. We present only in this paper the model that best agrees with the dielectric response of the insulation system. It is a simple 4-parameter structure R m -L-(R p // (R-C)) derived from the visual analysis of the current charging/discharging the winding insulation. R m

¨ ¢ ! " © " ¨ ¢ © #
As shown in Fig. 5, this model structure explains the shape of the measured current as the sum of two currents and . The function and g of the state-space representation (1) are obtained considering the capacitance voltages and the inductance currents as state variables.

Fig 6 shows the good agreement between the measured and simulated currents for the estimated parameters of proposed model. Table I gives the parameter means and standard deviations for ten experimental records for each T°C.

In Fig. 7, we present the relative evolution of the estimated parameters according to temperature changes, where is the estimated parameter at ambient temperature (30°C). The identified resistance is still almost constant for all temperatures; this should be explained by the fact that we keep always the same excitation frequency provided by the pulses generator, then the skin effect remains the same and keeps resistance unchanged. Nevertheless, this evolution is clearly not consistent with the temperature dependency of the cupper resistance. This tends to prove that the R lumped element of the model structure does not deal with a physical resistance. Furthermore, a net increase is noticed for the capacitance with the increase of test temperature. At 160°C, it is about 8% higher than its initial value. This evolution is of course due principally to temperature increase and not to insulation aging. Actually, since the insulation capacitance is a critical part that could define the winding insulation end of life [START_REF] Perisse | Robust diagnostics of stator insulation based on high frequency resonances measurements[END_REF], this important information should be taken into account in establishing a monitoring protocol with the ability to distinguish between insulation aging and temperature variations.

V. CONCLUSION

Operational conditions such as temperature, frequency and moisture affect the dielectric response of electrical generators insulation system. We have proposed in this study to investigate the short effect of temperature changes on estimated parameters of diagnostic model. It appears that the insulation resistance and inductance of a simple lumped model structures are not considerably affected by temperature variation, while insulation capacitance is clearly increased, comparing to its initial value, with the increase of temperature, this result is consistent with other results shown in section III. The utility of this information is to take into account the capacity evolution due to temperature variation, and propose diagnosis models able to make the difference between this evolution and evolution due to insulation degradation. It should be noted that we realize in parallel, experiments of accelerated aging on

Fig. 1 .

 1 Fig. 1. Guiding principle for the research of a diagnosis model.

Fig. 2 .

 2 Fig. 2. Online insulation system excitation in the high frequency domain Fig. 3. Principle of the output error method, for a continuous-time model structure and a quadratic criterion.

Fig. 4 .Fig. 5 .

 45 Fig. 4. Experimental bench. Fig. 6. Experimental measurement for a step excitation protocol

TABLE I

 I Fig. 7. Parameters evolution according to temperator variationsstator winding insulation and results should be compared to results presented in this study to take into account aging and short time temperature changes effect on dielectric response of insulation. The aim is to propose better knowledge diagnostic models. We note also that simple behavior models are used here, however, an extensive study with more complex knowledge based models is recommended for further work, to propose diagnostic models with diagnosis indicator only related to the insulation aging.

																																													.				ESTIMATED VALUES OF THE MODEL PARAMETERS
		Mean					55.53					55.47					55.72					55.31				55.53	55.58	55.74	55.80	55.92 56.16	56.01	56.2	56.24	55.86
	:)	Std							0.22						0.28						0.18						0.26					0.29	0.24	0.23	0.26	0.24	0.20	0.28	0.25	0.30	0.24
		Mean					1.054					1.075					1.052					1.062				1.045	1.067	1.071	1.074	1.058 1.061	1.065	1.06	1.066	1.047
		Std x 10 -3				15.45					10.39					8.59						13.21				16.53	10.83	12.85	9.78	15.19 14.23	11.84	17.0	8.94	13.98
		Mean					0.949					0.938					0.949					0.951				0.959	0.963	0.967	0.979	0.991 0.995	0.100	0.10	0.102	0.103
		Std x 10 -3					3.43						2.59						2.89						6.48					1.57	3.68	1.98	5.10	3.36	3.88	2.15	3.01	2.49	4.22
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Yassine Amirat
email: yassine.amirat@isen.fr

Zakarya Oubrahim

Mohamed Benbouzid

On Phasor Estimation for Voltage Sags Detection in a Smart Grid Context

Keywords: Smart grid, voltage sag detection, Kalman Filter, power quality (PQ), Hilbert Transform (HT), symmetrical components

The advent of smart grids have urged a radical reappraisal of distribution networks and power quality requirements, and effective use of the network are indexed as the most important keys for smart grid expansion and deployment regardless. One of the most efficient ways of effective use of these grids would be to continuously monitor their conditions. This allows for early detection of power quality degeneration facilitating therefore a proactive response, prevent a fault ridethrough the renewable power sources, minimizing downtime, and maximizing productivity. In this smart grid context, this paper proposes the evaluation of signal processing tools, namely the Hilbert transform and the linear Kalman filter to estimate voltage phasor for voltage sags detection.

I. INTRODUCTION

Despite accumulated experienced in the conventional electric distribution networks, the task of power quality in distributed networks is still an art. It has become more challenging as far as the generation system is moved nearby the distribution level and this is achieved by using a set of micro grids and energy islands based on renewable sources, connected to the main grid [START_REF] Brown | Smart grid policies: an international review[END_REF][START_REF] Simões | A comparison of smart grid technologies and progresses in Europe and the US[END_REF][START_REF] Glinkowski | Advances in wind energy technologies in the context of smart grid[END_REF]. This allows better utilization of the grid by transforming the actual distribution network into intelligent, robust energy delivery system. Therefore, key considerations when deploying smart grids are their availability, reliability, resiliency and profitability. Figure 1 depicts an overview of this complex infrastructure. Today, research is focused on the use of state estimation concept in power quality issues and in particular power quality state estimators (PQSE) [START_REF] Girgis | A digital recursive measurement of on line tracking of power system harmonics[END_REF], [START_REF] Farzanehrafat | Power Quality State Estimators for Smart Distribution Grids[END_REF] (Fig. 2).The PQSE is covering many power quality areas such as harmonic state estimation, frequency estimation [START_REF] Choqueuse | Estimation of Amplitude, Phase and Unbalance Parameters in Threephase Systems: Analytical Solutions, Efficient Implementation and Performance Analysis[END_REF], transient estimation [START_REF] Girgis | A digital recursive measurement of on line tracking of power system harmonics[END_REF], and voltage sags estimation. In the context of smart grids, it is then difficult to deal with such a complex system through conventional procedures used in classical distribution networks for keeping it under control, operating at the desired quality of the service (QoS), and dealing with reliability regarding a smart grid complexity and uncertainty [START_REF] Fitzer | Voltage sag detection technique for a dynamic voltage restorer[END_REF]. This requires much more sophisticated computer-oriented monitoring than in a classical grid [START_REF] Simões | A comparison of smart grid technologies and progresses in Europe and the US[END_REF]. In terms of PQ standards, voltage, frequency and harmonics are the three main parameters that must be considered and controlled to acceptable standards whilst the power and energy balance is maintained. In this context, voltage sags estimation is indexed as an essential requirement for a condition monitoring system in order to meet PQ standards [START_REF] Gómez-Lázaro | Characterization and visualization of voltage dips in wind power installations[END_REF][START_REF] Ignatova | Space vector method for voltage dips and swells analysis[END_REF].

For voltage sag detection, there are various methods used in contemporary power systems. These methods are based on electrical quantity signatures analysis (current, voltage, power, etc.) [START_REF] Mcbee | Utilizing a smart grid monitoring system to improve voltage quality of customers[END_REF][START_REF] Milioudis | Enhanced protection scheme for smart grids using power line communications techniques-Part I: Detection of high impedance fault occurrence[END_REF][START_REF] Milioudis | Enhanced protection scheme for smart grids using power line communications techniques-Part II: Location of high impedance fault position[END_REF][START_REF] Orcajo | Diagnosis of electrical distribution network short circuits based on voltage Park's vector[END_REF]; and usually involves the use of reference frame transformations such as Park's vector [START_REF] Kamwa | Robust Detection and analysis of power system oscillation using Teager-Kaiser energy Operator[END_REF] or space vector [START_REF] Ignatova | Space vector method for voltage dips and swells analysis[END_REF][START_REF] Mcbee | Utilizing a smart grid monitoring system to improve voltage quality of customers[END_REF][START_REF] Milioudis | Enhanced protection scheme for smart grids using power line communications techniques-Part I: Detection of high impedance fault occurrence[END_REF][START_REF] Milioudis | Enhanced protection scheme for smart grids using power line communications techniques-Part II: Location of high impedance fault position[END_REF][START_REF] Orcajo | Diagnosis of electrical distribution network short circuits based on voltage Park's vector[END_REF][START_REF] Kamwa | Robust Detection and analysis of power system oscillation using Teager-Kaiser energy Operator[END_REF][START_REF] Ohrstrom | Fast Protection of Strong Power Systems with Fault Current Limiters and PLL-Aided Fault Detection[END_REF]; other methods are based on advanced signal processing tools [START_REF] Amirat | An ensemble empirical mode decomposition approach for voltage sag detection[END_REF][START_REF] Amirat | Smart grid voltage sag detection using instantaneous features extraction[END_REF][START_REF] Amirat | A smart grid voltage sag detector using an EEMD-based approach[END_REF] where the EEMD is investigated; but the wellknown method is based on the symmetrical components transformation [START_REF] Gerardus | Symmetrical Components in the Time Domain and Their Application to Power Network Calculations[END_REF][START_REF] Leborgne | Differences in voltage dip exposure depending upon phase-to-phase and phase-to-neutral monitoring connections[END_REF]. This work attempts to highlight the use of signal processing techniques for phasor estimation in the context of smart grids.

II. VOLTAGE SAGS DETECTION AND CHARACTERIZATION

Voltage events are characterized by their magnitude and duration. Depending on the magnitude of the variation they are classified as sags, swells or interruptions, and depending on their duration they can be classified as transient event, short term event or long term event as depicted in Fig. 3. Voltage sags are indexed as the most important power quality issue. They are classified as a short-term event and characterized as a deviation of the RMS supply voltage from a reference value with typical dip depths ranging from 0.9 to 0.5 p.u. of a 1 p.u. nominal [START_REF] Mcbee | Utilizing a smart grid monitoring system to improve voltage quality of customers[END_REF]; lasting from few milliseconds to few cycles. Voltage sags are caused by abrupt increases in loads such as phase-to-phase or phase to ground short circuits, they are also caused by abrupt increases in source impedance during a loose of connection [START_REF] De Carvalho | A PLL-Based Multirate Structure for Time-Varying Power Systems Harmonic/Interharmonic Estimation[END_REF].

Voltage sag characterization concerns events quantification through a limited number of parameters. These parameters depend on the field of study. However, main characterization methods use two parameters to determine the severity of a voltage sag: magnitude (or "remaining voltage") and duration [START_REF] Glinkowski | Advances in wind energy technologies in the context of smart grid[END_REF]. In the context of a smart grid, it is therefore important to know whether voltage sag exists and afterward estimate its duration. The most usual voltage sags signatures are presented in Fig. 4 [START_REF] Ignatova | Space vector method for voltage dips and swells analysis[END_REF]. During a voltage sag three-phase system balanced conditions are no longer valid leading to possible disastrous consequences on the user end-loads and on the smart grid itself. So, for voltage sag detection, let us consider the mathematical model of three phase voltages, during the sag voltage the phase to ground voltages are expressed by (1) [START_REF] Gómez-Lázaro | Characterization and visualization of voltage dips in wind power installations[END_REF]:

( ) 2 sin( ) ( ) 2 sin( ) ( ) 2 sin( ) a a a b b b c c c v t V t v t V t v t V t ω α ω α ω α  = +   = +   = +   (1) Where a v , b v , c v , ω , a α
, α b and α c are respectively the three magnitudes, the frequency, and the three initial phase angles of the corresponding voltage phases. For voltage sag characterization the most common path is the use of symmetrical components [START_REF] Leborgne | Differences in voltage dip exposure depending upon phase-to-phase and phase-to-neutral monitoring connections[END_REF], [START_REF] Math | Algorithms for Characterizing Measured Three-PhaseUnbalanced Voltage Dips[END_REF]. For the system described by (1) the symmetrical components calculation is carried out through the transform given by ( 2):

[ ]

012 abc V F V   =   (2)
Where 01 2 V denotes the associated phasor of the symmetrical components:

V V V V V V V V =         =                 (3)
Indices 0, 1 and 2 are referring to zero, positive and negative sequences respectively. [ ] F is the transformation matrix introduced by Fortescue, and it is expressed by ( 4):

[ ] 2 2 1 1 1 1 1 3 1 F a a a a     =       (4)
Where a represents the rotational operator given by ( 5): However, the system given by ( 1) is time-dependent and expressed in time-domain. Since the computation of the symmetrical components requires the use of the phasor representation; system (1) must be represented in a complex formalism described by ( 6 (  )

( ) ( ) 2
( ) 2 ( ) 2 a a b b c c j t a j t b j t c v t V e v t V e v t V e ω α ω α ω α + + + = = =        (6)
Therefore, the use of symmetrical components relies on the amplitude and phase identification, in other words on phasor representation as depicted in Fig. 5.

III. PHASOR ESTIMATION

Phasor estimation is an essential task in power grids and power quality; since the voltage phase and voltage amplitude determine the available active power and its flow. So phasors are useful tools for operating and monitoring the grid. For phasor estimation many techniques and methods are investigated according to the application [START_REF] Phadek | Synchronized phasor measurements and their application[END_REF]. The FFT is used to estimate the fundamental harmonic, but a major drawback of this method is the asynchronous sampling. To overcome this lack, an FFT-based sliding window was proposed in [START_REF] Fitzer | Voltage sag detection technique for a dynamic voltage restorer[END_REF]. Also the RMS value is used to estimate the amplitude of the phasor, but RMS is based upon averaging of sampled data during one cycle, so it cannot represent instantaneous information. As voltage sags lead to transient phenomena (voltage unbalance and voltage/current waveform disturbances) [START_REF] De Carvalho | A PLL-Based Multirate Structure for Time-Varying Power Systems Harmonic/Interharmonic Estimation[END_REF], it is obvious that the Fast Fourier Transform (FFT) and other techniques based upon it, are no longer valid if they have been used in some cases [START_REF] Fitzer | Voltage sag detection technique for a dynamic voltage restorer[END_REF]. Advanced signal processing techniques are therefore required to deal with the complexity and uncertainty associated with a smart grid. In [START_REF] Mcbee | Utilizing a smart grid monitoring system to improve voltage quality of customers[END_REF][START_REF] Milioudis | Enhanced protection scheme for smart grids using power line communications techniques-Part I: Detection of high impedance fault occurrence[END_REF][START_REF] Milioudis | Enhanced protection scheme for smart grids using power line communications techniques-Part II: Location of high impedance fault position[END_REF][START_REF] Orcajo | Diagnosis of electrical distribution network short circuits based on voltage Park's vector[END_REF][START_REF] Kamwa | Robust Detection and analysis of power system oscillation using Teager-Kaiser energy Operator[END_REF], a Teager-Kaiser energy operator has been proposed for power system oscillations detection and analysis. However, this operator is highly affected by noises. In [START_REF] Barros | Applications of Wavelet Transform for Analysis of Harmonic Distortion in Power Systems: A Review[END_REF][START_REF] Labos | Application of wavelets and Prony method for disturbance detection in fixed speed wind farms[END_REF][START_REF] Phadke | A new measurement technique for tracking voltage phasors, local system frequency and rate of change of frequency[END_REF] and [START_REF] Hauer | Initial Results in Prony Analysis of Power System Response Signals[END_REF], wavelets and prony method were used. Wavelets however require properly windowed disturbing events to ensure accurate computations. Moreover, prony method highly depends on the system parameters and operating modes. Also the PLL algorithm was investigated too in [START_REF] Ribeiro | Power Systems Signal processing for smart grids[END_REF], [START_REF] Farzanehrafat | Power quality State Estimator for Smart Distribution Grids[END_REF]. It seems that the focus should be on transient signal processing techniques. In this work, it is therefore proposed to assess two non-stationary signal processing techniques based on Kalman filter and Hilbert transform.

A. Kalman Filter

The Kalman filtering has been mostly applied in system control applications; it is also the basis of a large variety of power system methods such as amplitude and phase tracking even in noisy signals [START_REF] Girgis | A digital recursive measurement of on line tracking of power system harmonics[END_REF], [START_REF] Phadke | A new measurement technique for tracking voltage phasors, local system frequency and rate of change of frequency[END_REF]. In this section we are concerned with its application to power system signals for voltage amplitude and phase estimation.

For this purpose let us consider the voltage i v (i = a, b or c), since this signal is sampled, it can be expressed by [START_REF] Gagnon | Wind Farm -Synchronous Generator and Full Scale Converter[END_REF]:

Temporal to Phasor representation

Phasor representation to symmetrical components representation

Phasor transformation Symmetrical components

v a (t) v b (t) v c (t) V a V b V c V 0 V 1 V 2
Fig. 5. Scheme for temporal to symmetrical components representation.

[ ] [ ] [ ]

,cos

1 k h h V n S n n δ = = + ∑ (7)
and [ ] ( )

,cos cos h h h h S n a nω α = + (8)
Where h is the harmonic component, k is the harmonic number in the model and δ represents the noise.

So, for the sample (n+1) th ( 7) can be written as follow:

[ ] ( )

,cos

cos ( 1)

h h h h S n a n ω α + = + + (9)
After trigonometric development (8) becomes:

[ ] ( ) [ ] ( ) ( ) [ ] ( ) ,cos ,sin ,
+ = + - + ⋯ (10) [ ] [ ] ( ) [ ] ( ) ,cos ,cos ,sin 1 cos sin h h h h h S n S n S n ω ω + = - … … (11) [ ] [ ] ( ) [ ] ( ) ,sin ,cos ,sin 1 sin cos h h h h h S n S n S n ω ω + = - … … (12)
The state equation is then:

[ ] [ ] [ ]

1 h h S n AS n n δ + = + ( 13
)
Where A is a 2 2 k k × matrix expressed by:

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 1 1 1 1 cos sin 0 0 sin cos 0 0 cos sin 0 0 sin cos k k k k A ω ω ω ω ω ω ω ω   -       =   -       ⋯ ⋯ ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ ⋮ ⋯ …
Finally the estimated amplitude and phase are:

[ ] [ ] ( ) [ ] ( ) 2 2 ,cos ,sin ˆk h h S n S n S n = + (14) [ ] [ ] [ ] ,sin ,cos arctan h k h S n n S n ϕ   =       (15)
and finally,

[ ] [ ] [ ] ,sin ,cos arctan h k h h S n n n S n α ω   = -⋅       (16)
Since the principal interest in phasor measurement is to calculate the fundamental component [START_REF] Phadek | Synchronized phasor measurements and their application[END_REF], this yields to express the phasor of [ ]

V n denoted [ ] V n as fellow: [ ] [ ] [ ] ˆj n V n S n e ϕ = ⋅ (17)

B. Hilbert Transform

Hilbert transform has been extensively applied to estimate the instantaneous amplitude and the instantaneous phase. It is usually more robust against noise. In this section, we are concerned with its application to power system for phasor voltage amplitude and phase estimation.

Let us consider the temporal description of three phase voltages given by [START_REF] Brown | Smart grid policies: an international review[END_REF].

( ) ( ) ( )
( ) 2 sin i i v t V t t ϕ = (18)
The Hilbert transform of the instantaneous voltages i v is given by ( 19) [START_REF] Oppenheim | Discrete-Time Signal Processing[END_REF]:

i i i HT V V t V t d t t t τ τ π π τ +∞ -∞ =   * = ∫   - ( ) ( ) ( ) 1 1
That yields to represent the real function ( ) i v t by its analytical signal ( ) S t expressed by ( ( ) S t can be any voltage or current function):

( ) ( ) ( ) i i i S S j HT t t S t = + ⋅     (20)
( ) ( ) ( )

ˆj t i i e S t S t ϕ = (21)
Since voltages are sampled, the application of HT requires the use of Discret Hilbert transform (DHT) given by:

[ ] [ ] { } { } [ ] 1 DHT S n TF TF S n u n -   =   (22)
where {} . [ ]

1, 0, 2 2, 1, 2, ....., 1 2 0, 1, ..., 1 2 N n N u n n N n N  =    = = -    = + -   (23)
Where N is the data sample number.

Using [START_REF] De Carvalho | A PLL-Based Multirate Structure for Time-Varying Power Systems Harmonic/Interharmonic Estimation[END_REF], the instantaneous amplitude and instantaneous phase in discrete formulation are given by:

[ ] [ ] ( ) [ ] ( ) 2 2 Ŝ n S n DHT S n = +     (24) [ ] [ ] [ ] arctan DHT S n n S n ϕ         =     (25)
Finally the frequency deviation can be estimated using the relation:

( ) ( ) ( ) 0 ( ) 1 2 s f n F n n f ϕ ϕ π ∆ = - -- (26)
Where s F corresponds to the sampling frequency and 0 f the rated frequency (50 Hz for the European grid, 60 Hz for American grid).

IV. SIMULATION AND RESULTS

To assess the ability of the two approaches for phasor estimation, a simulated system was investigated. The concerned system is depicted in Fig. 6. Figuire 6 case, described in [START_REF] Gagnon | Wind Farm -Synchronous Generator and Full Scale Converter[END_REF], allows steady-state operation of wind Farm and its dynamic response to voltage sag resulting from a remote fault on the 120-kV system. Figure 7 clearly shows that due to transmission line impedance and transformers impedance, the sag voltage has not the same effect on both sides of the grid. In this context, and to ensure that the grid will have islanding capabilities it is more convenient to track voltages variations in wind turbine side. After applying the proposed approaches to each phase (i.e, ( ) a v t , ( ) b v t and ( ) c v t ) in their sampled representation, phasors are extracted according to [START_REF] Ohrstrom | Fast Protection of Strong Power Systems with Fault Current Limiters and PLL-Aided Fault Detection[END_REF][START_REF] Amirat | An ensemble empirical mode decomposition approach for voltage sag detection[END_REF] and [START_REF] Gagnon | Wind Farm -Synchronous Generator and Full Scale Converter[END_REF][START_REF] Barros | Applications of Wavelet Transform for Analysis of Harmonic Distortion in Power Systems: A Review[END_REF].

Figures (8a, 8b, 8c and 8d) illustrate estimated phasors (amplitude and phase) at given time, and the corresponding symmetrical components. These results agree with the theory of symmetrical components. In order to assess the error of both methods, the instantaneous amplitudes and instantaneous phases of phasors, a V , b V and c V are presented in Fig. 9 and Fig. 10 respectively. Errors are calculated assuming that the real frequency is 60 Hz and the real amplitude is one in p.u.. Results are reported in Table 1. Hence, to characterize the voltage sag, amplitudes of positive, negative, and zero components are depicted in Fig. 11. It shows that both methods can estimate the voltage phasor in order to extract the symmetrical components, and present some drawbacks. Kalman filter requires a knowledge about the fundamental frequency and it needs a convergence time. Since Hilbert transform needs no knowledge about voltage parameters, but it requires a large window (more cycles) to estimate phasor parameters and presents the edge effect problem. V. CONCLUSION This paper dealt with the assessment of signal processing tools, namely the Hilbert transform and the Kalman Filter, for phasor estimation in order to detect voltage sags in a context of smart grids. The performance of both methods has been evaluated for a simulated grid. The usefulness of these methods is their low complexity. The achieved results clearly show that Hilbert Transform and linear Kalman filter can be used for voltage sag detection. However, further investigations are required to evaluate their performance of these methods regarding other electrical parameters.

Fig. 1 .

 1 Fig. 1. A smart grid topology [© SAET].

Fig. 2 .

 2 Fig. 2. State estimation techniques [20].

Fig. 3 .

 3 Fig. 3. Voltage events classification.

Fig. 4 .

 4 Fig. 4. Voltage sag main signatures [8].

  - correspond to the FFT and Inverse FFT, respectively, and ( ) u n is defined by (21) :

Figure 7

 7 Figure 7 depicts therefore the three phase voltages ( ) a v t , ( ) b v t and ( ) c v t respectively, measured on bus bar D during a voltage drop of 30% in one phase in bus A side. A key aspect in power systems is which parameters should be measured and analyzed in order to get the best characterization.

Fig. 6 .

 6 Fig. 6. Simulated system for voltage sag detection.

Fig. 7 :

 7 Fig. 7: Three phase voltages during a sag voltage in both sides of transmission line.

8 .

 8 Phasors representation and the corresponding symmetrical component.

Fig. 9 .

 9 Fig.9. Amplitude variation of phasors.

Fig. 10 .

 10 Fig.10. Instantaneous phases and frequency estimation.

Fig. 11 .

 11 Fig.11. Amplitude variation of symmetrical components.

Table 1 :

 1 ESTIMATION ERRORS

		Kalman filter	Hilbert transform
	max( ) (%) a ∆	0.0409	0.0632
	max( )(%) f ∆	0.4165	0.2513