Mohsen M Y Damavandi

Parsa Moghaddam

M.-R Haghifam

M Shafie-Khah

João S Catalão
email: catalao@ubi.pt

M P Moghaddam

J P S Catalão

Modeling Reserve Ancillary Service as Virtual Energy Carrier in Multi-Energy Systems

Keywords: Energy hub model, multi-energy system, reserve ancillary service

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Increasing the share of distributed energy resources (DER) in the energy service provision confronts policy makers with new challenges and opportunities in energy system studies. These new facilities introduce a dependency in both energy carriers and time domains [START_REF] Mancarella | MES (multi-energy systems): An overview of concepts and evaluation models[END_REF]. Moreover, new independent decision makers enforce a high level of operational data to the energy system managers. In this situation, cloud-based engineering systems facilitate cooperation among independent decision makers and increase the utilization of inherent flexibility in the multi-energy systems (MES).

MES is an integrated energy system that considers the energy and information interaction between energy players and carriers simultaneously [START_REF] Mancarella | Real-Time Demand Response From Energy Shifting in Distributed Multi-Generation[END_REF]. These interactions have been modeled by "Energy hub system" and "matrix modeling" approaches ( [START_REF] Geidl | Energy hubs for the future[END_REF] and [START_REF] Chicco | Matrix modelling of small-scale trigeneration systems and application to operational optimization[END_REF]). In these models, MES is divided into some super-nodes that can interact with various energy carriers via interconnectors. Each super-node (energy hubs in energy hub system and distributed multi-generations in matrix modeling) consists of energy converters and energy storages, delivering the required energy service by transforming input energy carriers.

The proposed models have been developed in [START_REF] Schulze | Modeling and optimization of renewables: applying the Energy Hub approach[END_REF]- [START_REF] Mancarella | Real-Time Demand Response From Energy Shifting in Distributed Multi-Generation[END_REF] and new considerations such as renewable energy resources, demand side management, and demand response have been included in MES's mathematical modeling. Moreover, in other power system studies, such as reliability index assessment and cascading failure mitigation, MES economic evaluation have been considered [START_REF] Bozchalui | Optimal Operation of Residential Energy Hubs in Smart Grids[END_REF]- [START_REF] Haghifam | Reliability and availability modeling of combined heat and power (CHP) systems[END_REF].

In [START_REF] Galus | Provision of Load Frequency Control by PHEVs, Controllable Loads, and a Cogeneration Unit[END_REF] the energy hub model has been developed to consider integration of plug-in electric vehicles (PEV) in grid to vehicle (G2V) modes as a manageable load for ancillary service provision (frequency control). Moreover, the capability of MES to serve ancillary services has been discussed in [START_REF] Mancarella | Integrated energy and ancillary services provision in multienergy systems[END_REF] and the new concept of multienergy/power arbitrage has been developed to consider reserve of distributed multigeneration.

In this paper, the reserve ancillary service is modeled on an energy hub approach as virtual output energy carriers that can be injected to the macro-MES level. The spinning reserve is provided from combined heat and power (CHP) units and electric storages. For CHP units, reserve provision is defined as the capability of the unit to increase its output electric power to its operational limits.

In addition, for electric storages this capability is restricted by power interaction with the system and stored energy. The energy hub model is developed to consider both storage and converter reserve provision in the mathematical model. Some rows are added to the output energy vector to model reserve energy as a virtual port, and consequently the system coupling matrix will be changed to consider the share of each energy element in the reserve provision. The numerical results demonstrate the proficiency of the proposed model and the energy scheduling adjustment of micro-MES in the operation time horizon.

Contribution to Cloud-based Engineering Systems

By increasing the dependency among energy carriers, conventional methods for system management are no longer effective. Thus, decision makers in the energy sector try to propose multi-dimensional models for energy systems that can cover technical, economic and environmental aspects of these systems. In this way, MES can be considered as a cloud-based engineering system consisting of various energy players and carriers.

This cloud-based perspective of the energy system increases collaboration among energy players from both energy and information points of view. Moreover, the huge amount of management data enforces energy players to develop novel computational methods to decrease system operation time and manage unwanted contingencies. The cloud-based engineering system may entail two integrated data and energy layers in such a way that energy players share information with each other for increasing their operational flexibility in both normal and contingency conditions.

In this regard, proposing standard models for system players can enable energy and information interactions among players. Since energy hub approach as a modular model has these capabilities, nowadays; researchers in energy studies try to develop new models to include more characteristics of the energy system.

In this paper, the energy hub model is developed from a mathematical point of view to consider reserve ancillary services for a micro-MES energy player. The proposed model accelerates cooperation of the micro-MES in the cloud-based engineering system by increasing their profit from two individual energy markets. Moreover, the energy hub presentation of the reserve service as a modular approach helps system managers to exchange data among energy players more efficiently.

Energy Hub Modeling

The energy hub model consists of a conversion matrix (C) that transforms input energy carriers (p) to the required energy services (l). For assessing the impact of energy storages in MES, ref. [START_REF] Adamek | On Decisive Storage Parameters for Minimizing Energy Supply Costs in Multicarrier Energy Systems[END_REF] added a new vector (ė) to the input energy vector that demonstrates the changes in stored storages' state of charge (SOC). Matrix S determines the share of energy storages in the output energy vector. Moreover, one of the main energy hub approach assumptions is unidirectional energy flow. Therefore, ref. [START_REF] Schulze | Modeling and optimization of renewables: applying the Energy Hub approach[END_REF] has added the vector k in the output of the energy hub to inject the surplus energy in the system to the upstream energy network.

[ ][ ] [ ]

C p = l (1) [ ] [ ] [ ] k l e p S C + =       & (2)
In this paper, the electric reserve in MES is defined as the capability of MES to increase its injected electric energy to the upstream network. In the mathematical approach, this capability is considered as virtual energy output port that injected the required service to the upstream network. Therefore, new rows (r ) are added to the vector k to determine the supplied reserve services. In addition, corresponded zero rows are added to the vector l.

  =       old new inj k k r ,   =     old new l l 0 (3)
By adding new rows in the output, the matrices C and Swill be modified to determine the share of each element of the new output energy service (electric reserve).For the energy converters, the reserve service is defined as the capability of the energy converter to increase its output energy to its operational limits. Furthermore, for energy storages this capability is restricted by their output power and stored energy in each hour. Therefore, determining the reserve service for electric energy storages needs new rows in p to show the share of electric energy storage for serving reserve to the MES as an input virtual energy carrier (r ).

        old new St p p = r (4)
        old new EC St C 0 C = C C , 0   =     old new S S (5)
where: C : coupling matrix that states the conversion of inputs energy carriers into outputs energy services. C : coupling matrix to show the share of energy converters in output reserve which is based on the efficiency of energy converters. C : coupling matrix to show the share of storage in output reserve which is based on the discharge efficiency of storage. S : storage coupling matrix that shows the changes of output energy service versus changes in the stored energy. M: matrix of vacant capacity of energy converters U: decision making matrix with binary arrays which determines the participation of each converter in output reserve.

In order to produce C , each array of M is divided by the corresponding array of P and after that are multiplied by array of U( 6). (6) By substituting the modified terms in(1),the system new equation is as [START_REF] Mancarella | Real-Time Demand Response From Energy Shifting in Distributed Multi-Generation[END_REF] and [START_REF] Bozchalui | Optimal Operation of Residential Energy Hubs in Smart Grids[END_REF].

        = +           new new new new new p C S l k e & (7)
                                  old old old old old St EC St inj p C 0 S k l r = + 0 C C 0 r e & (8)

MES's Operational Framework

In this paper the MES is divided into three layers, namely as macro-MES, micro-MES and multi-energy demand (MED). Fig. 1 demonstrates a micro-MES that receives input energy carriers from macro-MES and serves required energy services to the MED. Furthermore, the micro-MES is equipped by energy converters (e.g. CHP units and auxiliary boilers (AB)) and energy storages (e.g. heat storage (HS) and electric storage (ES)). These facilities enable micro-MES to participate in both energy and ancillary service markets to increase its profit. Micro-MES operator maximizes its profit by participating in energy and reserve markets and interacting energy with MED (9).

Q r r FOR r π π π π π ρ π ρ π ρ η π    - - + + +      + - -  ∑ (9)
The operational constraints for micro-MES are described as follows:

Input energy carrier: micro-MES energy interaction with macro-MES is restricted by the interconnectors' capability to transfer energy flow.

in in in inj inj t t t W w w r W - ≤ - - ≤ ( 10
)
in in t G g ≤ ≤ 0
(11) CHP unit: The CHP unit converts input gas to the required heat and electric power. The heat and power output should be lower than units' maximum operational limits and their ratio should be equal to a predetermined parameter ( ).

0

CHP CHP CHP t t w r W ≤ + ≤ , 0 CHP CHP t q Q ≤ ≤ ( 12
) CHP t CHP t CHP w q = λ (13)
AB operational constraints: The output heat of AB should be lower than its operational bound.

AB AB t AB Q q Q ≤ ≤ (14)
Storages interaction limit: Rate of HS and ES interactions with Micro-MES should be in its operational zone.

HS HS t q Γ ≤ & , ES ES t w Γ ≤ & (15)
Decision variable constraint: is dispatch factor and shows the share of each energy element of input energy and its amount should be between zero and 1.

≤ AB t g CHP t g v v 1 , 0 , , ≤
, = + AB t g CHP t g v v (17) (16) 1 ,
General energy hub conversion matrix: energy hub conversion matrix is represented in (18), transforming input energy carriers to the required energy services.

(

v η W g w v η v η Q r r q G v g η g w η η η                       + = +                           -         & & (18)

Numerical Results

The micro-MES operator receives gas and electricity from macro-MES and delivers MED's required heat and electricity. Figs.2 and 3 depict the MED's consumption and price signals for input and output energy carriers.

Three cases are prepared to determine the operational behavior of micro-MES's elements in various system operating conditions. In cases I and II, micro-MES is equipped with CHP, AB, and ES, while in Case III the system also has a HS. Case I: In this case, the micro-MES operator does not participate in the reserve ancillary service. Figs. Case II: In this case, besides participating in energy trade, the micro-MES sells reserve service to the macro-MES. Fig. 6 shows the CHP unit and ES reserve provision in micro-MES. In most of the operation period the ES reserve provision is restricted by its discharge rate, while for CHP unit the reserve provision restriction is more related to the share of CHP unit in electricity demand satisfaction. Moreover, Fig. 7 compares the ES behavior in Cases I and II. In Case II, ES is more eager to maintain its SOC level after hour 6 since the reserve price is acceptable for reserve provision to participate in the reserve ancillary service, instead of energy provision.

Case III: In this case, HS is added to the micro-MES's equipment. Fig. 8 depicts the share of each element in micro-MES electricity balance. The CHP unit can be operated during hours 2, 3 and 15-17 when there is no heat demand, but the surplus heat can be stored in HS. Furthermore, Fig. 9 determines that in Case III the Micro-MES prefers to utilize its equipment to provide energy to maximize its profit, instead of participating in the reserve service provision. It means that reserve service and HS are two sources to increase the degree of freedom of the micro-MES operator. Adding both of these resources decreases the impact of each independent source.

Conclusions

In this paper, reserve ancillary service has been modeled in MES. The energy hub mathematical model was developed to consider reserve for both electric energy converters and storages. The numerical results have determined that the micro-MES operator for electric energy converters prefers to participate in energy trade instead of reserve operation. On the contrary, for electric storage it was more profitable for the operator to maintain its SOC to participate in reserve provision service, instead of the energy trade. As a matter of fact, ES behaves like a converter that consumes in the off peak of electricity as primary resources and delivers electricity in the peak hours of price and its operation restricted by its capacity. Therefore, it is less efficient than conventional energy converters (e.g. micro-turbines and gas engines) to produce electricity and deliver to the energy market. On the other hand, its characteristics (e.g. fast ramp) make it suitable to participate in ancillary services. Moreover, in Case III it

Fig. 1 .

 1 Fig.1.MES schematic considering reserve ancillary service as virtual port.

(

Fig. 2 .

 2 Fig.2. Electricity and heat consumption of MED.

Fig. 3 .

 3 Fig.3. Left side: Input energy price. Right side: output energy price.

  4 and 5 demonstrate the share of each element in electricity and heat balance of micro-MES. The CHP unit produces energy in hours 5, 11-14, and 18-22, while MED consumes heat and the energy price is almost high. Moreover, ES stores energy in hours 6-8 and provides a part of electricity demand in hours 16 and 17,when the maximum electricity price in the operation time horizon occurs.

Fig. 4 .

 4 Fig.4. Electricity operation scheduling of micro-MES in Case I.

Fig. 5 .

 5 Fig.5. Heat operation scheduling of micro-MES in Case I.

Fig. 6 .

 6 Fig.6. Reserve provision in micro-MES in Case II.

Fig. 7 .

 7 Fig.7. ES behavior in Case I and Case II.

Fig. 8 .

 8 Fig. 8. Electricity operation scheduling of micro-MES in Case III.

Fig. 9 .

 9 Fig. 9. CHP and total reserve provision comparisons in Case II and Case III.

Acknowledgment. This work was supported by FEDER funds (European Union) through COMPETE and by Portuguese funds through FCT, under Projects FCOMP-01-0124-FEDER-020282 (Ref. PTDC/EEA-EEL/118519/2010) and PEst-OE/EEI/LA0021/2013. Also, the research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007-2013 under grant agreement no. 309048

was shown that each system has various sources of flexibility for the operator (e.g. storage and participation in various markets), having cross impacts that influence each other performance.


M Seixas

R Melício
email: ruimelicio@gmail.com

V M F Mendes

M Collares-Pereira

M P Dos Santos

Simulation of Offshore Wind System with Three-Level Converters: HVDC Power Transmission in Cloud Scope

Keywords: Offshore wind turbine, HVDC, three-level converter, simulation

This paper is on a simulation for offshore wind systems in deep water under cloud scope. The system is equipped with a permanent magnet synchronous generator and a full-power three-level converter, converting the electric energy at variable frequency in one at constant frequency. The control strategies for the three-level are based on proportional integral controllers. The electric energy is injected through a HVDC transmission submarine cable into the grid. The drive train is modeled by a three-mass model taking into account the resistant stiffness torque, structure and tower in the deep water due to the moving surface elevation. Conclusions are taken on the influence of the moving surface on the energy conversion.

Introduction

Over the last decades, the energy primary sources like oil, coal, nuclear and natural gas have been used as the main source of energy for conversion into electric energy [START_REF] Tao | A feasibility study of a stand-alone hybrid solar-windbattery system for a remote island[END_REF]. Nonetheless, disadvantageous circumstances are pointed out in the use of these sources, for instances: for fossil-fuel, the harmful on the environment and the cost subject to market instability and tending to increase in the long run. Forecasts for nonnuclear and non-renewable energy sources are in support of an envisaged scarcity pointing to the exhaustion on those sources in less than a century [START_REF] Ishugaha | Advances in wind energy resource exploitation in urban environment: A review[END_REF]. Exploitation on renewable energy sources have emerged by support of country polices or by more indepth protocols such as the Kyoto Protocol as a response to harmful on the environment, instability and increasing fuel costs.

The wind energy source is the one within the exploitation of renewable energy sources that has experienced a greater development [START_REF] Mckenna | Cost-potential curves for onshore wind energy: A high-resolution analysis for Germany[END_REF]. This development has achieved such a level that there is an increasing difficulty in new deployment, i.e., a difficulty to find new appropriate places for onshore wind farms. Particularly, a considerable exhaustion on convenient places is reported for Europe [START_REF] Soukissian | Use of Multi-Parameter Distributions for Offshore Wind Speed Modeling: the Johnson SB Distribution[END_REF]. Thus, there is an interest in the exploitation of offshore wind energy, which has the benefits of available vast sea areas and of more favorable wind conditions, than those on land. Higher values of wind speed with less variation, because of the non existence of obstacles are encouraging offshore deployment [START_REF] Lange | Importance of thermal effects and sea surface roughness for offshore wind resource assessment[END_REF]. Deployment of offshore wind energy has a power transmission technology depending on the distance from the floating platform to onshore: for distances less than 60 km is proposed to use highvoltage ac (HVAC); for longer distances is proposed high voltage dc (HVDC) [START_REF] Gil | Power generation efficiency analysis of offshore wind farms connected to a SLPC (single large power converter) operated with variable frequencies considering wake effects[END_REF].

Deployment of offshore wind energy has a less concern with visual and noise impact, allowing the use of wind turbines with larger rotors, allowing a higher value for the installed power. The increase in size of the wind turbines implies particular design. As fact the blades are larger, more flexible and tend to bend. Therefore, the model for the drive train has to capture this fact and this paper proposes a three-mass (TM) model to take into account the concern with the dynamic of the blades, the structure and the moving floating platform.

Relationship to Cloud-based Solutions

The global growing demand for energy led to the deployment of new energy conversion called distributed energy located closely to the electric load to be served. A variety of small grid-connected devices referred to as distributed energy resources (DER) are introduced to allow the flow of distributed energy. DER systems typically use renewable energy sources, and increasingly play an important role for the electric grid. With the increasing demand for electricity and the growth of distributed generation (DG) there is a concern with the ability of the existing electric grid to accommodate the needed transformation. Thus, new insight into the electric grid is required and the concept of Smart Grid (SG) is on the way. SG conception includes the area of smart technologies able to quantify in real time the energy consumption, the power quality and ability to avoid or pre-detect eventual faults [START_REF] Ayodele | Challenges of Grid Integration of Wind Power on Power System Grid Integrity: a Review[END_REF]. Vital for the SG conception is an architecture that guaranties safety and reliability. A layered processing system seems a good option, gathering information about the SG components delivering an insight into the behavior of the system, which allows operators or machines to take action accordingly, over a services platform. The designed software and hardware structure can be transferred to a cloud architecture system, benefiting of existing Internet Cloud Services, as in Fig. 1, to process the data storage and data transfer in real-time with cheap processing costs [START_REF] Ayodele | Challenges of Grid Integration of Wind Power on Power System Grid Integrity: a Review[END_REF]. Due to safety issues, one expects that the best approach to the deployment of the cloud architecture in electrical grids will be a private cloud. Private clouds are projected and built exclusively for a single organization, which has full control over how applications and data are deployed in the cloud [START_REF] Mell | The NIST Definition of Cloud Computing[END_REF]. Every step in the project raises security issues that must be addressed to prevent vulnerability [START_REF]Is a private cloud really more secure?[END_REF]. Due to the vulnerability, the SG approach advocates the use of a layered system that supports the integration of sensors and actuators, acting in a bidirectional data exchange, thus providing an introduction in the accelerating integration of SG services [START_REF] Batista | Layered Smart Grid Architecture Approach and Field Tests by ZigBee Technology[END_REF].

Modeling

The wind speed intermittence and variability stochastic character are possible to model by a deterministic sum of harmonics ranging 0.1-10 Hz as is indicated in [START_REF] Akhmatov | Advanced Simulation of Windmills in the Electric Power Supply[END_REF] given by

] ) ( sin 1 [ ) ( ∑ + = k k k o t A u t u ω ( 1
)
where u is the wind speed with perturbation, o u is the average wind speed, k A is the magnitude of the k eigenswing, k ω is the eigenfrequency of the k eigenswing. Offshore structures are influenced by marine wind and wave dynamics. The marine wave model [START_REF] Eikeland | Compensation of wave-induced motion for marine crane operations[END_REF] is given by

1 ( , , ) ( ) cos[ ( ) ( ) ( )( cos( ( )) sin( ( ))] n a i x y t i i t i i x i y i η η ϑ ε φ ψ ψ = = + - + ∑ (2)
where ) , , ( t y x η is the wave elevation for ) , ( y x position as a function of time, a η is the vector of harmonic wave, ϑ is the vector of harmonic wave frequencies, ε is the vector of harmonic wave phases (random), φ is the vector of harmonic wave numbers and ψ is the vector of harmonic wave directions. The offshore wind system (OWS) apart from the wind turbine has a rectifier linking a permanent magnet synchronous generator (PMSG) to a first set of two capacity banks. A high voltage DC transmission submarine cable links the first set of two capacity banks to a second one connected to an inverter, injecting the energy into an electric grid through a second order filter. The OWS is shown in Fig. 1.

( 1 bs db t b t T T T J dt d - - = ω (3)
) ( 1 hs dh sm bs th h h T T T T T J dt d - - + + = ω (4) ) ( 1 g dg hs g g T T T J dt d - - = ω (5)
where t ω is the rotor angular speed at the flexible blades part, b J is the moment of the inertia of the rotating parts at the flexible blades part, t T is the mechanical torque, db T is the resistant bearing torque due to the damping at the flexible blades part, bs T is the resistant shaft stiffness torque between the flexible blades part and hub, h ω is the rotor angular speed at the hub plus rigid blades part, h J is the moment of the inertia of the rotating parts of hub plus the rigid blades part, th T is the mechanical torque of the rigid blades part, sm T is the tower and platform stiffness torque due to floating surface motion, dh T is the hub bearing resistant torque, hs T is the shaft stiffness torque between hub and generator, g ω is the rotor angular speed at the generator, g J is the moment of inertia of the rotating parts of the generator, dg T is the generator bearing resistant torque, g T is the electric torque.

The equations for the PMSG modeling can be retrieved in diverse texts [START_REF] Ong | Dynamic Simulation of Electric Machinery: using Matlab/Simulink[END_REF]. In the PMSG to avoid demagnetization of permanent magnet a null reference stator direct component current * 0 sd i = is imposed [14]. The three-level converter has twenty-four unidirectional commanded insulated gate bipolar transistors (IGBTs) to implement the rectifier or the inverter functionality [START_REF] Seixas | Fifth Harmonic and Sag Impact on PMSG Wind Turbines with a Balancing New Strategy for Capacitor Voltages[END_REF]. The group of four IGBTs associated in the same phase is said to be the converter's y leg. The switching variable y n with

} 2 , 1 , 0 { ∈ y n
is used to identify the IGBT i state in the leg y of the three-level converter. The switching variable y n is given by the switching function of the IGBT. The switching variable as shown in [START_REF] Seixas | Fifth Harmonic and Sag Impact on PMSG Wind Turbines with a Balancing New Strategy for Capacitor Voltages[END_REF] is given by The level variable jny

     = = = = = = = 0 ) ( 1 ) ( , 0 0 ) ( 1 ) ( , 1 0 ) ( 1 )
δ with } 2 , 1 { ∈ j
used to establish the charging state of the capacitor banks [START_REF] Seixas | Fifth Harmonic and Sag Impact on PMSG Wind Turbines with a Balancing New Strategy for Capacitor Voltages[END_REF] is given by

   ≤ > = y y y jn n j n j 1 0 δ (7)
The rectifier output voltage is given by

∑ ∑ ≠ = = - = c y l a l CRj l jn j y jn sy U u ) 2 ( 3 1 2 1 δ δ } , , { c b a y ∈ (8)
The DC voltage at the first set of capacity banks dcRj U rectifier side is given by

∑ = + = 2 1 1 j CRj dc Rj dcR i C C dt dU ( 9
)
The linking between the first set of two capacity banks on offshore to a second one on onshore is assumed to be by a high voltage DC transmission submarine cable.

The submarine cable is modeled by a π equivalent circuit [13]. The current dc i in the cable is given by

) ( 1 dc dc dcI dcR dc dc i R U U L dt di - = (10)
The DC voltage at the second set of capacity bank dcI U , inverter side, is given by

∑ = + = 2 1 1 j CIj dc Ij dcI i C C dt dU ( 11
)
The electric grid is modeled by an equivalent three-phase active symmetrical circuit given by a series of a resistance and an inductance. Hence, the electric current injected, into the electric grid is given by

) ( 1 y fy n fy n fy u i R u L dt di - - = } , , { f e d y = (12)
This model is normally used in transient simulations and corresponds to what is said to be an infinite grid model linked by the equivalent series impedance.

Control Method

A classical PI controller is used to acquire the current references. IGBTs on/off switching states imply that the three-level converter is a time variant system. Therefore the sliding mode (SM) control strategy is crucial to alter the three-level converter, assuring the assortment of the proper space vectors. Additionally, PWM by space vector modulation (SVM) associated with SM is used for triggering the IGBTs. The SM strategy's aim is to let the system slide along a predefined dynamic, also known as sliding surface ) , ( t e A αβ , by changing the system structure, i.e., by changing the IGBTs on/off state. The IGBTs can only switch at finite frequency, due to physical limitations causing the control current not to be able to follow exactly the reference current, thus creating an error αβ e . Hence, to maintain the system within the neighboring of the sliding surface, the state trajectory must comply with the conditions [START_REF] Seixas | Fifth Harmonic and Sag Impact on PMSG Wind Turbines with a Balancing New Strategy for Capacitor Voltages[END_REF] given by ( , ) ( , ) / 0 A e t dA e t dt

αβ αβ < (13)
A small error is acceptable in practice and a convenient achievement of this strategy is accomplished with hysteresis comparators [START_REF] Seixas | Fifth Harmonic and Sag Impact on PMSG Wind Turbines with a Balancing New Strategy for Capacitor Voltages[END_REF]. If the current error, given by the comparators hysteresis output αβ σ is quantified in five levels is possible to relate the space vectors with the current error. So αβ σ are integers numbers taking

values in the set } 2 , 1 , 0 , 1 , 2 { - -
. There are redundant vectors which correspond to different voltage level selection. The control strategy carries out a reduction on the capacitors banks unbalancing voltage by considering three vector tables [START_REF] Seixas | Fifth Harmonic and Sag Impact on PMSG Wind Turbines with a Balancing New Strategy for Capacitor Voltages[END_REF], which take in account the voltage level of each capacitor bank by the defining the vector to be used. The output vectors for the level 0 , level 1 and level 2 in the β α plane for the three-level converter are shown in Fig. 2. The instantaneous current injected into the electric grid is shown in Fig. 5. The average THD for the current injected into the electric grid is 0.53%. Hence, the THD of the output current is lower than the 5% limit imposed by IEEE-519 standard.

Conclusions

Increased wind power penetration leads to new technical challenges, transient stability and power quality. In this paper, a model for a simulation study is presented for offshore wind system equipped with a PMSG with a back-to-back neutral point clamp three-level power converter with HVDC transmission. The case study presented is intended to evaluate the offshore wind system behavior, taking into account the wind and the marine wave perturbations. The simulation carried out is in favor that the perturbation due to the marine wave has a negligible effect on the system performance. This fact is due to two situations: first the wave frequency is smaller when compared to the frequency of the wind gust; and second the floating platform is stable. So, the system feels little mechanical vibrations due to the marine waves. The simulation study revealed a good performance of the proposed offshore wind system with the three-level power converter and HVDC link. Although, there are effects on the current output of the power converter, the average value for the THD of the current injected into the electric grid is lower than 5% limit referred as to be imposed by IEEE-519 standard.

t ω 2 θFig. 1 .

 21 Fig. 1. Offshore wind system with three-level converter, HVDC. The drive train is modeled by a three-mass model. The first mass concentrates the inertia of the flexible part of the blades; the second mass concentrates the rigid part of the blades, hub, tower and platform, discarding the movement between the different elements, but including the floating motion influence on the second mass; the third mass concentrates the inertia of the generator. The equations for modeling the motion of the drive train are based on the torsional version of the second law of Newton and are given by

)

Fig. 2 .Fig. 3 .Fig. 4 .

 234 Fig. 2. Three-level inverter, output space vectors.

Fig. 5 .

 5 Fig. 5. Current injected into the electric grid.

Acknowledgments. This work was partially supported by Fundação para a Ciência e a Tecnologia, through IDMEC under LAETA, Instituto Superior Técnico, Universidade de Lisboa, Portugal.


Kaloyan Mihaylov
email: kaloyan.mihaylov@gmail.com

Rui Neves-Medeiros

Rumen Arnaudov

Stanimir Valtchev

Investigation of the Tesla Transformer as a Device for One-Wire Power and Signaling and as a Device for Power and Signaling Through the Ground

Keywords: single wire power, single wire signalling, power through the ground, ground communications, Tesla coil, Tesla transformer, quarter wave resonator

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The idea for wireless electricity is not new. The first experiments in this field began in the end of the 19th century. The most fascinating proposal was the Tesla's idea to send electrical power to every point on the globe by utilizing a giant low-frequency transmitter [START_REF] Tesla | Patent #787412: Art of transmitting electrical energy through the natural mediums[END_REF]. Most of his experiments are associated with the production of very high voltages for fun, although this is not the case. As it will be seen below, the electrical discharge or sparking is an unwanted side effect of his famous transformer. However, the scale of the Tesla's construction doesn't permit easy reproduction of those effects. Scaled-down models could be used to investigate the working principle of the transformer.

The main topics presented in the paper are the transformers construction, single wire experiments and ground experiments. Chapters 3, 4 and 5 describe the basic principles that we have obeyed during the building of the coils and conducting the experiments, and in chapters 6 and 7 there are the results.

In chapter 3 it is stressed that the secondary coil of the transformer behaves like a distributed-elements circuit, not as lumped elements one.

In chapter 4 is represented the method to find the resonant frequency of the coil. It is the starting point for energy transmission, because other frequencies doesn't work.

Due to its nature, in chapter 5 we have suggested the most useful methods to calculate the secondary coil of a Tesla transformer. The fully theoretical approach isn't precise enough, therefore we have focused our attention on more robust methods.

In chapter 6 we have shown the results of single wire power transfer. There are successful results.

Chapter 7 is dedicated to ground transmission experiments. We haven't got any measurable result for ground transmission. Because there are some successful attempts for ground transmission by other researches, some directions were given.

Relationship to Cloud-based Solutions

The focus of cloud computing is usually computational performance, storage, network connectivity and collaboration. In order to deeply influence everyday life, a lot of small devices are brought in the network. Remote data acquisition and control techniques require a good network connectivity and power supply. However, there is no "one for all" solution. A lot of different tools exist for data or power transmission, like low-power wireless transceivers, optical transceivers and energy harvesting devices of different kind.

The aim of our experiments is to discover new methods for data transmission together with secure power supply. The single wire solution has shown its potentialities. Small portions of power were successfully transmitted over short distances. The true utilization of only one wire could enable further development and introduction of sensor nodes or electronic devices of other choice.

This method could be used in places where energy harvesting modules cannot bring the power needed to operate a device. Another advantage is the more robust communication between the devices (for example in noisy wireless environment). Drawback of the system is the need of a conductor. However there are some developments that investigate the possibilities to use the ground instead of single wire. The circuits require much higher power levels and the propagation of the power is different from the single wire system, but the resonator coil and the principles are the same. Despite the lower frequencies, the ground is another medium that could be added to the existing communication solutions.

The results in this area of research give us conviction, that these methods will soon be ready to take place in the civil and industrial development, hence ensuring wider connectivity of different devices to the cloud.

Common Construction Mistakes

As it was stated above, the most common mistake is the consideration of the Tesla's resonator as a lumped element system [START_REF] Corum | Tesla Coils and the Failure of Lumped-Element Circuit Theory[END_REF]. Solution of the inductance and the capacitance for a lumped element would not reveal any useful information for the constructor. The most common formulas of Wheeler and Nagaoka are valid for DC or low-frequency designs.

In fig. 1 there is a typical example of poorly designed secondary. It consists of 5550 turns of 0.18 mm solid copper wire. Height of the coil is 1 m, and the diameter of the coil is 2 cm. The use of very thin wire leads to higher pure resistance, hence lower Q factor of the coil. Another problem of such a coil is its height to diameter ratio. The higher length to diameter ratio increases the self-capacitance of the coil [6.3, p. 452]. It was found that the coil shown in fig. 1 has great amount of variable capacitance, dependent on the objects near the test setup. Therefore it has also low Q factor and weakly detected resonant frequency. According to [3, p. 452, table 1] the single-layer solenoids have lowest capacitance when their height to diameter ratio is about 0.5 ÷ 2. Due to its high selfcapacitance, the coil could be used as a sensor.

Experimental Approach for Detecting the Coil's Resonant Frequency

Since the secondary of a typical Tesla Transformer is described by the distributed element approach, it should be indicated what current/voltage distribution is expected. In fig. 2 there is the general case of a typical secondary coil. The current in the base of the coil is high and diminishes along the height of the coil. The voltage in the base is small, but increases along the coil height.

If the primary coil is impulse-fed, then the secondary oscillates on its natural resonance. The output voltage is measured with a scope's probe on top of the coil, without direct connection. The grounding pinch of the probe should be removed for accuracy. Ordinary signal generator could be used, even simple PWM circuit as SG3525 or TL494 to drive the primary, but only to find the resonant frequency of the secondary. In fig. 3 the oscilloscope probe #1 is connected to one of the SG3525 outputs. The drive frequency is about 60 kHz. The second output is connected to a 1-turn primary of a arbitrary chosen air-core coil.

Note: the phase difference between channel 1 and channel 2 is because of the second output used to monitor the drive frequency. The actual voltage waveform of the first output (connected to the primary) does not correspond to the waveform of channel 1, due to the loading. The drive impulse exact time is marked with a red line on the oscillogram.

The resonant frequency of the secondary coil is about 250 kHz. The experimenter should tune the drive frequency and duty cycle to achieve maximum amplitude. Also one must be careful of nearby objects around the coil. The human body strongly influences the parameters of the secondary, especially if it has a lot of turns and bigger self-capacitance.

Calculation of the Secondary Self-Resonant Frequency

In section 3 the most critical mistakes were given. To summarize, they are the use of thin wire and the high height to diameter ratio. Solid copper is preferred. The authors of the paper have the view that multistranded wire could be more suitable for the purpose if the separate strands are not insulated from each other. The insulation of the strands leads to higher inter-strands capacitance, hence lower frequency and Q of the coil.

The best method for coil calculation that we found suitable for our purposes is the GeoTC JavaScript library by the Tesla Secondary Simulation Project [START_REF] Nicholson | Tesla Secondary Simulation Project: Document pn2511[END_REF]. We have found that the calculations of the non-loaded bare (without top-load) secondary are within the pointed error limit. Other programs like E-Tesla6 (by Terry Fritz) could be more suitable if better accuracy is sought. A mixed Java/JavaScript web application based on GeoTC is FanTC (http://www.classictesla.com/fantc/fantc.html) which is used in our experiments.

Experimental Results for One-Wire Transmission

To conduct the experiments two pieces of plane PCB transformers were used. The characteristics of the two types of transformers are given in table 1. In each of the cases the shorter coil (primary) is the exiting or load coil, and the longer coil (the secondary) is the resonator coil. The outer ends of the two secondaries are connected via 70 cm multistranded wire. The inner end of each secondary is connected to a 20 cm bolt used for capacitive loading of the resonator. The circuit schematic is shown in fig. 5. R1 has the value of 10 Ohms and is used to measure the input current. R2 has variable resistance and it is used to measure the maximum power point. The diode bridge is compound of 4 BAT754 Schottky diodes with 200mV of voltage drop for each diode. At a distance of up to 3 m there was mutual inductance between the coils. Any presence of power cords pushes an error in the measurements. The human body and movements around the transformers does influence the working conditions of the system.

Conclusions

The objectives of the experiments were examination of the possibilities to use a quarterwave transformer as a power source for single wire power cord or power transmission through the ground. Two types of plane PCB transformers were used. To minimize RF interference and EM radiation from the system, the lower frequencies were preferred. Power levels of about 1 at the receiver's side were obtained, which satisfies some of the modern low-power electronic devices (such as the STM32L-series MCUs). These power levels are DC values after a bridge rectifier, so there is room for improvements.

The utilization of the system with ground as a medium for power transmission didn't gave measurable results. The measurements included bare oscilloscope connection to the receiver's secondary without amplifier. However, there are promising results of utilization of a 136 kHz Tesla Coil transmitter with detectable signal at 5.5 km distance. [START_REF] Peterson | Yahoo! Group "wireless_energy_transmission[END_REF]. Due to the properties of the soil, a different type of resonators should be used. The working frequency should be lower, hence bigger coils. The drive circuit should be optimized for higher power levels.

It should be noted that the aim of the experiments isn't maximum efficiency, but prove of the effect. Hence the system's performance is highly influenced by a lot of factors. The coupling between the primary and the secondary, mutual inductance, rectifier's efficiency, matching and other important factors are left out of consideration.

Fig. 1 .

 1 Fig. 1. Example of poorly constructed quarter-wave resonator coil.

Fig. 2 .

 2 Fig. 2. Representation of the voltage-current distribution along a resonator coil.

Fig. 3 .

 3 Fig. 3. Oscillogram of one of the driving channels of SG3525 and the response of the resonator.

Fig. 4 .

 4 Fig. 4. Example of one of the transformers used in the experiments.

Fig. 7 .

 7 Fig. 7. Test setup of the ground transmission system.

Table 1 .

 1 Characteristics of the transformers.

	Parameter	Transformer 1	Transformer 2
	Number of turns (secondary)	60	100
	Wire width	0.882 mm	0.353 mm
	Turn spacing	0.482 mm	0.635 mm
	Inner diameter	0	0
	Outer diameter	163.7 mm	197.6 mm
	Length of wire	15.426 m	31.038 m
	DC Inductance	193.3	648.3

Part of the data for a test setup which utilizes Transformer 1 is shown in Table 2. In the "Note" column there is a remark of how the secondary coil is loaded. In order to achieve maximum Q of the coil, minimum capacitance should be introduced. However, it was found that some capacitance should be added for better energy transmission. The details of how exactly the capacitive loading influences the power transmission are not deeply studied yet.

Also it should be noted, that in the power balance there is a big amount of loss in the R1 resistor, which is used to measure the current in the primary coil and great voltage drop at the receiver's diode bridge. In table 3 the values of the experiments with "Transformer 2" styled coils are given. There was no measurable output power. The diode bridge DB1 was removed so as the secondary of T2 to be open circuit. Then a measurement on both ends of the circuit was conducted with digital oscilloscope, but no signal was detected.


M Alves

J M Dias Pereira
email: dias.pereira@estsetubal.ips.pt

J M Fonseca

A Piezoelectric Device for Measurement and Power Harvesting Applications

Keywords: Regenerative energy sources, energy harvesting, mechanical vibration energy, stand-alone systems, sensor applications

With the fast growth of wireless communications between nodes/sensor units, devices installed in remote places require power energy supply solutions to assure their functionally and data communication capabilities. For these applications energy harvesting takes place as a good solution, to increase the availability of energy, in opposition to the conventional systems of energy supply. Regenerative energy sources like thermoelectric, magnetic, piezoelectric, and/or renewable sources such as photovoltaic, wind, among others, allowed the development of different powering solutions for sensor units. The purpose of this work is to characterize a piezoelectric device to measure and capture mechanical vibrations from equipments, structures and piping vibrations, as well as from other sources. The study is carried out taking into account the power supply capabilities of piezoelectric devices as a function of the amplitude and frequency of the vibration stimulus, as well as, the electrical characteristics of the load circuit.

Introduction

Nowadays, with electronics becoming smaller and requiring less power, the energy harvesting has began to be a topic of great importance to self-powering systems. The energy available in the surrounding environment of an application can be a viable option to allow the development of energy autonomous systems or to extend the autonomy of systems supplied by storage elements, usually batteries.

Different sources that capture the energy of the environment, like solar, wind, vibration and temperature gradients, have been target of recent developments to improve their performances and efficiency.

A type of environmental source as a global solution to supply a wide range of applications is not feasible, since the requisites of power/energy can be completely different in amplitude or frequency/time. Many energy harvesting systems are oversized, either because there is no controller to manage and optimize the energy flow or because they are designed for worst-case scenarios [START_REF] Kimball | A System Design Approach for Unattended Solar Energy Harvesting Supply[END_REF]. Create a dedicated harvesting solution with low power electronic internal consumption, is crucial to improve the performance of energy harvesting systems.

The main problem of autonomous and self-powered devices is related with energy storage solutions. The energy that is not consumed by the system is generally requested when the source cannot deliver the amount of energy that guarantees the consumption needs. Two main storage solutions can be used: batteries and supercapacitors. The use of supercapacitors has the following advantages: life cycle, which is at least two orders of magnitude higher than the corresponding one of lead acid batteries; long operation time in a temperature range of -40ºC to +60ºC; higher power capability; ability to deliver for short time intervals electrical energy at significantly higher power than batteries [START_REF] Obreja | On the Performance of Commercial Supercapacitors as Storage Devices for Renewable Electrical Energy Sources[END_REF]. Batteries are usually indicated for long time supplies (energy) without requisites of instantaneous high consumption (power).

The scaled solution purposed here aims to verifying the possibility of suppling enough energy for monitorization of physic variables in instrumentation applications. This energy must also be sufficient to assure the wireless communication of data.

A. Piezoelectric Elements

Piezoelectric material produces mechanical strain under the influence of an externally applied electrical field, and conversely produces electrical potential in response to an applied mechanical strain.

The goal of this work is to characterize a piezoelectric device to convert vibration mechanical energy to electrical voltage, not only as a measurement signal, but as lowpower energy source. In the instrumentation field it is possible to use this principle for energy harvesting in the measure of force and wind, and in some type of flowmeters (Figure 1). At the same time, they give the information about the wind speed, it is possible to use the range of voltage generated for supplying the integrated application. The measuring principle is based on development of Karmen Vortex shedding street in the wake of a body built into the pipeline. Vortex shedding creates alternate pressure conditions whose frequency is directly related with flow velocity, being the relationship, between these variables, given by: .

1 d f S V th × × = (1) 0,2 < Sth < 0,21 to 300 < R < 150000 Sth -Strouhal number R -Reynolds number

B. Cantilevered-beam Configuration

Solutions for piezoelectric energy harvesting are typically based on a cantileveredbeam configuration (Figure 2). The piezoelectric beam is clamped at one end and the other end is allowed to oscillate freely in response to vibration normal to the flat surface of the beam, converting these vibrations to in-plane material strain. It is extremely important that the natural frequency of harvester configuration is tuned to match the vibration source. Vibration sources vary considerably in amplitude and dominant frequency. In most of environments, vibration is not made up of a single frequency but is typically made up of a set of main fundamental frequencies and associated harmonics. [START_REF] Gilbert | Farooq: Comparison of Energy Harvesting Systems for Wireless Sensor Networks[END_REF] Fig. 2. Model of a cantilever-beam with piezoelectric ceramic layers A cantilevered-beam piezoelectric energy harvester is a complex electromechanical system in which the electrical and mechanical loading of the beam are interrelated. The beam dimensions and tip mass determine the resonant frequency of the beam, which is tuned to match the dominant vibration frequency of its environment. These typically small vibrations are mechanically amplified. A development and fabrication process of a cantilever-beam with a thin film piezoelectric power generator is presented by Jeon et al. [START_REF] Jeon | MEMS power generator with transverse mode thin film PZT[END_REF]

Main Contributions -Relationship to Cloud-based Engineering

This work focuses a technological solution for power harvesting, with the aim at increasing the autonomy of low-power consumption applications. The present contribution intends to characterize a piezoelectric device as a solution to increase the energy sustainability and performance of instrumentation systems with wireless communication capabilities. This systems located in remote places have the ability to actualize information located in cloud data base computing systems. Particularly important for systems located in places where the viability of conventional energy supply is very expensive or technically difficult and it is crucial to minimize maintenance requirements. Figure 3

Methodology

In this work two electrically isolated piezoelectric elements were used, whether independently or bridged for increased voltage (series configuration) or current output (parallel configuration). Series connection will double the open-circuit voltage compared to a single element, and the effective capacitance will be 1/2 of the single capacitance. Parallel connection will double the current compared to a single element configuration, and doubling the effective capacitance. The option was to use the parallel connection in order to increase the power harvesting capability. However, the single element configuration was also tested and the results are presented as a reference for analyses facility. Each solution (single and parallel connection) was assembled in a cantilever-beam configuration.

The AC voltage at terminals of piezoelectric generator is in a first stage converted to DC voltage (Figure 4). The DC Voltage connected to a load permits defining the capability of generating power for instantaneous consumption, or for energy storage when connected to a supercapacitor.

The open circuit voltage (VOC) and the short circuit current (ISC) are the two main parameters to start this system characterization (Figure 5). Furthermore, achieving the Maximum Power Point (MPP) in function of load for different amplitude and frequency vibration is important to optimize the performance of this type of energy harvesting. To maximize transfer efficiency, the load must be matched to the piezo's equivalent impedance. Theoretically, the piezo impedance at a given amplitude and frequency, as well as the load impedance, can be thought as a pair of simple (but unknown) impedances which make up an impedance divider. The power transfer between the two impedances is optimized when their resistive and reactive values match. This corresponds to the point at which the piezo's loaded voltage is equal to half its open-circuit voltage.

The simplicity of the solution that is proposed intends to represent an advantage in terms of internal power consumption. This circuit (Figure 4) is ideal for directly powering small sensors that can perform their function (e.g. record or transmit a measurement). It could also be used to extend battery life, of devices with their own battery/capacitor power source.

Results and Critical View

The cantilever-beam configuration tested with piezoelectric elements is strongly dependent on the environment frequency vibration, and not only on vibration amplitude. The system can be mounted in a large number of applications, but if for some reason the frequency of vibration change, the performance as harvester source will be very poor. This can be an important limitation for harvest energy from environments that do not have a constant vibration but depends of external factors like ambient conditions. However in some applications the vibration frequency is well know, like for instance, the structure of an AC motor working at 230V/50Hz. In this case the natural frequency of the piezoelectric harvester element must be tuned to 50hz to match the vibration source. In a cantilever configuration the frequency tuning is performed by adding mass to the end of the cantilever solution. The tuning mass can be adjusted until the vibration frequency and the natural frequency of the piezoelectric device are equal. The beam dimension associated with the mass is also determinant to achieve the resonant frequency of cantilever, and consequently the maximum power that will be possible to capture. In most applications it is very important, to characterize the frequency of the environment vibration in which the element will be operating.

In this work the results were obtained for a established frequency of 50Hz. This frequency matches the vibration of an AC motor structure. We have also tested frequencies out of the tuned cantilever configuration, and it could confirm that, in this condition, the voltage level, and consequently the power harvesting, was extremely low. This short band wide of the cantilever with piezo elements is limitative as harvesting source and also as transducer for variables measurement. One solution to solve this problem, could be using different cantilever assembled in parallel each one tuned at a different frequency. The DC output voltage (Vout) shown in figure 6 is lower than expected, due to the voltage drop in each diode (0,7V). Since low power consumption is involved, it is possible the use of Schottky diodes with voltage drops as low as 0,15V. Figure 7 shows the power harvesting behavior as a function of different load impedance, keeping the amplitude vibration constant. A clear difference was observed depending on the assembly in beam of a single or double piezoelectric elements. The power harvesting increased 3 fold for the majority of loads tested and MPP it was obtained for lower load impedances. This is significant to determine the load connected and also for the level and period necessary for charging the storage element. By comparing figure 6 and 7 it is possible to conclude that the power increase obtained corresponds to only a small increase in the output voltage. This is mainly due to the increase in current owed to parallel configuration. The results presented in figure 8 and 9 correspond to a established frequency of 50Hz and 2 piezoelectric elements connected in parallel. The piezoelectric elements output was rectified and connected to a capacitive storage of 470 µF. The voltage at the terminals of the capacitor and the time necessary for charging are represented for different amplitudes of vibration. We can observe that the storage element subjected to the highest vibration amplitude could charge 6 times higher level of voltage during only the double-time to charge. Tests with capacitors of higher value and the same configuration of two piezo elements were done as well. Lower levels of voltage and higher period of charging were observed in this case, showing that the power harvesting is very low for storage elements with higher capacity.

Taken together these results allowed to verify that good levels of power harvesting are possible when several piezoelectric elements assembled in parallel, which may have a promising application.

Conclusion and Further Work

The piezoelectric materials are very sensitive to the surrounding vibration, producing easily very good levels of voltage. This characteristic is undoubtedly very important for vibration measurement. The use of these elements for power harvesting are however limited by the fact that the levels of voltage are not followed by good values of power supplies capabilities, exhibiting low current values. One hypothesis to solve the problem is the use of several piezoelectric elements connected in parallel. By using this method the power harvesting by piezoelectric elements can be a promising solution for low power consumption applications. Since this method can only be used in application where the environment vibration frequency has a low bandwidth, the best approach is to start by characterizing the vibration variation and the levels of power harvesting required.

New type of materials and ceramics are now being tested and developed to improve the piezoelectric characteristics. This will enable new values and facilities for power harvesting and measurement solutions and will probably greatly improve the efficiency and application of this method.

Fig. 1 .

 1 Fig. 1. Vortex flowmeter principle using piezoelectric elements

Fig. 4 .Fig. 5 .

 45 Fig. 4. Equivalent electric circuit AC-DC converter

Fig. 6 .

 6 Fig. 6. Output Voltage in function of Load value

Fig. 7 .

 7 Fig. 7. Power harvesting in function of Load

Fig. 8 .Fig. 9 .

 89 Fig.8. Capacitor Voltage in function of Vibration Amplitude with f=50Hz

  represents the block diagram of a sensing node with power harvesting and wireless communication capabilities.

	Environment
	Energy Source/
	Transducer	Transducer
	Energy Storage	Processing Unit	Wireless Communication Module
		Buffer
		Managment
	Fig. 3. Sensing node with power harvesting


S Kazem Hoseini

Edris Pouresmaeil

Jafar Adabi

João P S Catalão
email: catalao@ubi.pt

Stable Integration of Power Electronics-Based DG Links to the Utility Grid with Interfacing Impedance Uncertainties

Keywords: Voltage Source Converter, Distributed Generation, Interfacing Impedance, Fractional Order Control, Active Sliding Mode Control

For the integration of distributed generation (DG) units to the utility grid, voltage source converter (VSC) is the key technology. In order to realize high quality power injection, different control techniques have been adopted. However, the converter-based DG interface is subject to inevitable uncertainties, which adversely influence the performance of the controller. The interfacing impedance seen by the VSC may considerably vary in real distribution networks. It can be observed that the stability of the DG interface is highly sensitive to the impacts of interfacing impedance changes so that the controller cannot inject appropriate currents. To deal with the instability problem, this paper proposes an enhanced fractional order active sliding mode control scheme for integration of DG units to the utility grid, which is much less sensitive to interfacing impedance variations. A fractional sliding surface which demonstrates the desired dynamics of the system is developed and then the controller is designed in two phases: sliding phase and reaching phase to keep the control loop stable. The proposed controller takes a role to provide high quality power injection and ensures precise current tracking and fast response despite uncertainties. Theoretical analyses and simulation results are verified to study the performance and feasibility of the proposed control scheme.

Introduction

The power network is experiencing structural changes due to increasing concerns about greenhouse gas emissions, energy cost, and security of conventional power generations as more distributed generations (DGs) are integrated to the utility grid [START_REF] Akorede | Distributed energy resources and benefits to the environment[END_REF]. These DG technologies are mainly used to deliver clean energy from renewable energy sources such as solar power, wind power, micro turbine generation plants and fuel cell plants, through power electronic voltage source converters (VSCs) [START_REF] Blaabjerg | Overview of control and grid synchronization for distributed power generation systems[END_REF].

Stable operation of the control loop in DG system is an important aspect; an instable control loop cannot inject desired power from DG source to the grid. The interfacing impedance seen by the VSC may noticeably change depending on the configuration of the utility grid (as DG units are commonly installed in weak grids), output filter design, and grid synchronization techniques. Also some phenomena such as cable overload, temperature effect, and saturation often further aggravate the problem.

Uncertainties in the impedance seen by the VSC leads to instability in the control of DG interfacing system since the stability is so sensitive to the interfacing impedance variations. As a result of instable controller, the DG system cannot inject the expected currents or persistent oscillations may exist in the injected currents. Hence, it seems crucial to design a control structure for the DG system, which is insensitive to the variations of interfacing impedance.

Several control methods and studies have been reported for integration of DG sources into the power grid, however, few works have addressed instability problems of interfaced converters in DG technology. Influence of grid impedance on current control loop have been investigated in [START_REF] Liserre | Stability of photovoltaic and wind turbine gridconnected inverters for a large set of grid impedance values[END_REF] and a control technique based on adjusting the controller gain was proposed to stabilize interfaced VSC; however, it needs problematic online measurement of interfacing impedance; moreover, changing controller gain may deteriorate other capabilities such as disturbance rejection. An adaptive grid-voltage sensorless control method has been presented in [START_REF] Mohamed | Adaptive grid-voltage sensorless control scheme for inverter-based distribution generation[END_REF] for converter-based DG units, which needs an additional interfacing parameter estimator in a parallel structure to deal with the stability problem. Reference [START_REF] Mao | An adaptive controller for inverter-interfaced DGs connected to grids with a wide range of unknown impedances[END_REF] proposed a model reference adaptive control to connect DG units to utility grid through a LCL filter; nevertheless, it uses converter output current as the feedback signal to the controller, which may result in the resonance of the grid current due to the interaction of converter output harmonic current and the resonant circuit formed by the grid inductor and filter capacitor. An impedance-based stability criterion has been verified in [START_REF] Sun | Impedance-based stability criterion for grid-connected inverters[END_REF], which discusses the condition in which a grid-connected converter is stable, but it does not present a proper control solution for the instability problem. Effects of grid impedance variations on the stability of the interfaced converter have been investigated in [START_REF] Yang | A robust control scheme for grid-connected voltagesource inverters[END_REF]. It has been demonstrated that when either the inductance of the grid increases or the inductance and resistance of the grid increase together, the system stability is adversely affected. Also, [START_REF] Yang | A robust control scheme for grid-connected voltagesource inverters[END_REF] proposes an H∞ controller to deal with the instability problem; however, its implementation and obtaining weightingfunctions in such systems are very hard.

Hence, an effective control method is proposed in this paper, which combines fractional order control (FOC) with an active sliding mode control (ASMC). FOC, which is the use of non-integer order derivatives and integrals, has been realized as an alternative scheme for solving control problems [START_REF] Podlubny | Fractional-order systems and PIλDµ controllers[END_REF][START_REF] Valerio | Fractional robust system control[END_REF]. Applying the notion of fractional order for managing control problems is a step closer to the practical situations since the real world processes are mostly fractional. Some models of fractional order and fractional calculus have been developed to solve different control problems [START_REF] Agrawal | A general formulation and solution scheme for fractional optimal control problems[END_REF][START_REF] Jelicic | Optimality conditions and a solution scheme for fractional optimal control problems[END_REF]. Sliding mode control is a particular type of variable structure control systems designed to drive and then maintain the system states within a close neighborhood of the decision rule, and is well-known for its robustness to parameter variations and disturbances [START_REF]Modern sliding mode control theory: new perspectives and applications[END_REF][START_REF]Advances in variable structure and sliding mode control[END_REF]. Therefore, combining fractional order approaches together with sliding mode control has attracted significant interest [START_REF] Hosseinnia | Boolean-based fractional order SMC for switching systems: application to a DC-DC buck converter[END_REF][START_REF] Pisano | Sliding mode control approaches to the robust regulation of linear multivariable fractional-order systems[END_REF][START_REF] Efe | Fractional order sliding mode control with reaching law approach[END_REF].

The objective of this study is to extend a fractional order active sliding mode controller (FrASMC) to the current control of a grid-connected three-phase VSC for possessing small steady-state tracking error and fast response of the grid current while maintaining the system insensitive to interfacing impedance variations. In this control method, the system states are limited to lie on the fractional sliding surface where the dynamics of the system are only specified by the dynamics of the switching surface. The system is invariant in the control design, and the motion of the states trajectory is much less sensitive to disturbances and parameter variations. The control method is easy to implement and only needs nominal values of model parameters.

Contribution to Cloud-based Engineering Systems

Over the past two decades, declines in the costs of small-scale power generations increases in the reliability needs of many customers, and the partial deregulation of electricity markets have made DG technology more attractive to businesses and households as a supplement to utility-supplied power. Moreover, the environmental advantages can also be considered as a key-driving element accelerating the development of DG technology in power system. However, the increasing numbers of DG units in power system require new and intelligent control techniques for the operation and management of electric networks in order to maintain or even to improve the power supply reliability and quality in the future. As a consequence, DG system can be considered as a cloud-based system, consisting of an intelligent control technique for integration of DG sources to the power grid. This cloud-based aspect of energy technology will concentrate on several issues in design of future power grids based on Smart Grids technology during the presence and cooperation of suitable energy storage devices in the distribution grid. The main goal in this research area is design of a control technique for integration of DG sources into the power grid. In the proposed model, load behavioral patterns can be monitored and the huge amount of data can be harvested from utility and load sides. Its processing is being carried out using algorithms established for signal processing. The proposed control technique confirms the role of DG technology in cloud-based energy system by creating a safe operating region for operation of DG units and injection of power from DG sources into the power grid.

Modeling of a Grid-Connected DG Interface

This section focuses on a three-phase DG model with a current-controlled VSC. To form a grid-connected DG model, the VSC is integrated to the utility grid with an inductive filter and an isolation transformer. The filter is responsible for filtering out the high frequency harmonics generated by VSC switching actions, and the transformer works as an interfacing reactor. Fig. 1 shows the equivalent circuit diagram of the grid-connected DG unit with three-phase two-level VSC. The VSC synthesizes the AC voltage using space vector modulation (SVM) technique with the available DC voltage. Control signals for the SVM are generated from an appropriate controller, and switching pulses are applied to the VSC switches for power injection from DG source into the power grid. The utility grid is modeled as a three-phase AC source with internal impedance using Thevenin's Theorem. = + is the equivalent interfacing impedance seen by the VSC; ix is the converter output current; vgx is the grid voltage; and idc is the input DC current (x=a, b and c).

Dynamic Analysis of the Proposed Model

To inject high quality power to the utility grid, a current control technique is usually adopted to form the output voltage so that minimum current error is achieved. A VSC controls the magnitude and frequency of the output voltage. The output voltage at each leg of the VSC can be expressed as = .

(1) where vxN is the converter output leg voltage, and the switching variable determines the switching state of interfaced converter, and can be defined as 1 or 0 when the upper (or lower) switch of the leg is switched on or off. Dynamic equations of the proposed model can be calculated by applying Kirchhoff's law in AC-side of the interfaced converter as,

Two-Level VSC C DC-Link R L v ga v dc i a i c i b v gb
+ - + = 0 ( 2
)
where vxo is the converter output phase voltage. By defining the switching state function of the VSC as,

= - 1 3 , (3)
and substituting (3) in ( 2), the dynamic equations of the proposed model can be rewritten as,

+ - + = 0. (4)

Steady-State Analysis of the proposed Model

In synchronous reference-frame, a rotating reference-frame is utilized so that all fundamental positive sequence alternating variables become DC quantities in dq0coordinate system; then, objectives of controlling and filtering in the control loop of the system can be achieved easier. By Park transformation matrix, dynamic equations of the proposed model in ( 4) can transfer into the synchronous reference-frame with angular speed ω as,

- ! + - + = 0 (5)
! + + ! -! + ! = 0. (6)
It should be mentioned that zero components of line currents and grid voltages are zero since balanced operation and symmetric abc voltages are considered. Substituting time-varying components by the steady-state quantities and forcing time derivatives to zero, the steady-state expressions of the proposed model in equations ( 5) and ( 6) can be calculated as,

" = - !#$% + #$% + & * (7)
!" = #$% + !#$% & * . ( 8
)

Proposed FrASMC Design of Integrated DG Model

The current control structure of a three-phase VSC is the key element in the control design of a converter-based DG system. When the interfacing impedance seen by the VSC becomes highly inductive, the bandwidth of the controller is considerably decreased, which degrades the dynamic performance of the controller [START_REF] Yang | A robust control scheme for grid-connected voltagesource inverters[END_REF]. This leads the system to be oscillatory or even unstable. In this section, to deal with the instability problem a FrASMC is designed for the integration of DG units to the power grid with a three-phase framework. The proposed control scheme has the properties of fast response and small steady-state tracking error of the grid current while keeping the performance of the model insensitive to the interfacing impedance uncertainties. The current dynamics of the proposed model in the synchronous reference frame ( 5) and ( 6) can be represented by the following state-space equations,

where,

( = 1 ! 2 3 , ( = 1 ! 2 3 , ( = 1 ! 2 3 , ) = 4 -/ - -/ 6, * = 4 1/ 0 0 1/ 6 . , + = 4 -1/ 0 0 -1/ 6, and
0 = ∆) ( + ∆* ( + ∆+ ( , (10)
where, z=d and q; A, B and T are the system matrices; Ao, Bo, and To represent the nominal values of A, B and T; ∆), ∆*, and ∆+ denote the system parameter variations; and r expresses the lump of uncertainties caused by parameter variations, which is assumed to be bounded by 7,

|0| < 7, (11)
where, 7 is a specified positive constant.

Considering the described model, the errors dynamics can be defined as follows,

: = ; #$% -; = (#$% -( , (12)
where, izref (=xref) is the reference current. The objective of the FrASMC is to design the controller Uz such that the model state (x=iz) precisely tracks the reference output current.

Output of the PID controllers is a linear combination of the input, the integral of the input, and the derivative of the input. Fractional order PID controllers are generalizations of PID controllers. Output of the fractional order PID controllers is a linear combination of the input, a fractional integral of the input, and a fractional derivative of the input. The proportional-integral equation, which defines the fractional order PI (FrPI) [START_REF] Podlubny | Fractional-order systems and PIλDµ controllers[END_REF][START_REF] Hosseinnia | Boolean-based fractional order SMC for switching systems: application to a DC-DC buck converter[END_REF] control action can be written as, , . = < = : + < > ?@ : (

where < = , < > , and A are design parameters of the fractional order PI control. If A is 1, the result is a usual PI (called as integer PI). A switching surface is developed for interfaced converter based on FOC in order to design the FrASMC. In this sense, a fractional form of the linear compensation PI networks is used to achieve fractional sliding surface of the form PI @ . Based on the generalized (fractional) PI @ structure, a candidate for fractional sliding surface, which represents a dynamic of desired model can be obtained of the form as,

D = < = E; #$% -;F + < > ?@ E; #$% -;F; 0 < A < 2, (14)
Sliding mode control forces the system state space trajectories to reach the sliding surface in a finite time and to stay on the surface for all future time. The main function of the sliding mode controller is to switch between two different structures of the system so that a new type of system motion (sliding mode) exists on the surface. Therefore, the proposed FrASMC is designed in two phases as, 1-The sliding phase by D = 0 2-The reaching phase when D ≠ 0.

In the first phase (D = 0), which shows the model operates in desired condition and there are no parameter variations, the active controller can be designed as follows,

($! = -* ?J E) ; + + ( + K / DF, (15)
where K / is a positive definite matrix for placing poles of the nominal system in its desired values. The active controller works properly when there are no uncertainties; however, the sensitivity of the dominant poles of the current controller is very high to system uncertainties mainly due to parameter variations, which lead to drive harmonic currents through the converter or even the controller may become unstable, i.e. D ≠ 0.

In this phase, in order to drive any states outside the surface to reach the surface in a finite time, a switching control law is developed to construct the FrASMC as,

( = ($! + LMNO,D., (16)
where L is the switching factor, which can be tuned in order to eliminate the effects of parameter variations. As can be seen from ( 15) and ( 16), the nominal values of the model parameters are only required to design the controller.

Simulation Results

To investigate the performance of the proposed control strategy, the simulation models have been developed under Matlab/Simulink environment. The rated rms grid phase voltage is 120 V at 60 Hz, and the nominal values of interfacing resistance and inductance are = 0.2 Ω and = 0.1 QR, respectively. The dc-link voltage is set to 400 V and the switching frequency of the VSC is 10 kHz. To inject the desired active power S #$% to the grid, d-component of the reference current can be obtained as,

#$% = 2 3 S #$% . (17)
By setting a zero value for the q-component of reference current in the current control loop of DG unit, only active power will be injected by integration of DG sources into the power grid and power factor between the injected current from DG unit and load voltage will be obtained a unity value. The reference active power is set to 3 kW.

The stability and dynamic response of the DG model will now be analyzed, and the effectiveness of the designed controller will be evaluated and compared with conventional PI synchronous-frame current controller to validate the proposed procedure. To illustrate the effect of interfacing impedance variations on the current controller performance of a VSC-based DG system, the interfacing resistance and inductance seen by the VSC ( and ) change from [ = 0.2 Ω and = 0.1 QR] to [ = 0.5 Ω and = 0.6 QR] at the instant = 0.05 s in simulations.

The performance of the PI current controller under above mentioned condition is depicted in Fig. 2. As can be seen, the current response cannot track the reference current properly when the interfacing impedance changes, which is consequence of the nonrobust PI current controller. Tuning the PI gains can bring the system into stability; however, it leads to degradation of disturbance rejection capability and increase in steady-state tracking error.

Fig. 3 shows the output current of the VSC and utility grid voltage with the proposed FrASMC. It can be observed that the current controller remains stable and injects high quality currents in phase with the utility grid voltage when the interfacing impedance changes. From Fig. 3 the grid-connected power factor is 1.

Fig. 1 .

 1 Fig. 1. Equivalent circuit diagram of a generic grid-connected DG unit using three-phase VSC.

(

  = ) ( + * ( + + ( = ,) + ∆). ( + ,* / + ∆*. ( + ,+ + ∆+. ( = ) ( + * ( + + ( + 0,

Fig. 2 .Fig. 3 .

 23 Fig.2. DG current and its reference with conventional PI controller under interfacing impedance variation at t = 0.05 s

Acknowledgment. This work was supported by FEDER funds (European Union) through COMPETE and by Portuguese funds through FCT, under Projects FCOMP-01-0124-FEDER-020282 (Ref.

PTDC/EEA-EEL/118519/2010) and PEst-OE/EEI/LA0021/2013. Also, the research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007-2013 under grant agreement no. 309048.

= 0.05 s By drawing harmonic spectra of current response of conventional PI and proposed FrASMC before and after interfacing impedance change (with S #$% = 3 kW), following results have been observed. THD of output current of the conventional PI current controller before impedance change is 1.08%. However, the THD after impedance change is 6.27% ,which does not meet the THD requirement of the IEEE standard 1547 (that is below 5%) [START_REF]IEEE standard for interconnecting distributed resources with electric power systems[END_REF]. Furthermore, significant low order harmonics can be observed in the harmonic spectra. THD of VSC output current of proposed FrASMC before and after impedance change are 0.96% and 1.12%, respectively, which demonstrates the proposed controller successfully meets the standard requirement and has a better performance than conventional PI.

Conclusions

DG units are commonly integrated to the utility grid by VSCs for better controllability, reliability, and efficiency. The VSC transfers the generated power to the utility grid using a current controller that has the mission of injecting high quality currents. However, the stability of the current controller is often destroyed by the variations on the interfacing impedance seen by the VSC, such that the system becomes oscillatory or even unstable. In this paper, a FrASMC has been proposed for integration of DG units to the utility grid. The main aim of the proposed current controller is to deliver the desired power to the grid with low harmonic current, fast response, and high power factor while maintaining the system insensitive to the interfacing impedance variations. To validate the performance of the proposed FrASMC, simulation verification results have been presented and compared with the conventional PI current controller. The results show that high quality current injection is achieved with the proposed FrASMC for grid integration of VSC-based DG units.