A Unified Artificial Neural Network Architecture for Active Power Filters

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 1, FEBRUARY 2007

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A Unified Artificial Neural Network Architecture for Active Power Filters Djaffar Ould Abdeslam, Patrice Wira, Jean Mercklé, Damien Flieller, and Yves-André Chapuis

Abstract—In this paper, an efficient and reliable neural active power filter (APF) to estimate and compensate for harmonic distortions from an AC line is proposed. The proposed filter is completely based on Adaline neural networks which are organized in different independent blocks. We introduce a neural method based on Adalines for the online extraction of the voltage components to recover a balanced and equilibrated voltage system, and three different methods for harmonic filtering. These three methods efficiently separate the fundamental harmonic from the distortion harmonics of the measured currents. According to either the Instantaneous Power Theory or to the Fourier series analysis of the currents, each of these methods are based on a specific decomposition. The original decomposition of the currents or of the powers then allows defining the architecture and the inputs of Adaline neural networks. Different learning schemes are then used to control the inverter to inject elaborated reference currents in the power system. Results obtained by simulation and their real-time validation in experiments are presented to compare the compensation methods. By their learning capabilities, artificial neural networks are able to take into account time-varying parameters, and thus appreciably improve the performance of traditional compensating methods. The effectiveness of the algorithms is demonstrated in their application to harmonics compensation in power systems. Index Terms—Active power filter (APF), adaptive control, artificial neural networks (ANNs), harmonics, selective compensation, three-phase electric system.

I. INTRODUCTION

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NEXPLAINED computer network failures, premature motor burnouts, humming in telecommunication lines, and transformer overheating are only a few of the damages that quality problems may bring into home and industrial installations. What may seem like minor quality problems may lead to crucial situations in the future with the increasing proliferation of nonlinear loads. Indeed, nonlinear load currents and voltages are nonsinusoidal, and it is necessary to compensate for the generated harmonics and to correct the load power factor. For several

Manuscript received June 28, 2005; revised February 9, 2006. Abstract published on the Internet November 30, 2006. D. O. Abdeslam, P. Wira, and J. Mercklé are with the Faculté des Sciences et Techniques Laboratoire MIPS-TROP 4, Université de Haute Alsace, 68093 Mulhouse Cedex, France (e-mail: [email protected]; [email protected]; [email protected]). D. Flieller is with the Department of Electrical Engineering, Institut National des Sciences Appliquées (INSA), 67084 Strasbourg, France (e-mail: [email protected]). Y.-A. Chapuis is with the Institute of Industrial Science, University of Tokyo, Tokyo 106-8558, Japan (e-mail:[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIE.2006.888758

years, active power filters (APFs) have been recognized as advanced techniques for harmonic compensation [1]. Their objective is to recover balanced and sinusoidal source currents by the injection of compensation currents. APFs are very able to suppress the current harmonics and to correct power factor, especially in fast-fluctuating loads, in comparison to other compensation devices. In addition to their performances, APFs can favorably be inserted in existing power systems and are thus widely used in practical applications. A lot of recent research work investigates and tries to improve APFs by developing new topologies with power electronics technologies or new control laws. Mathematical modeling is also involved to formulate and characterize active and nonactive current and power for nonsinusoidal and nonperiodic waveforms in electrical systems. It also concerns numerical analysis, harmonic detection algorithm, control theory, and artificial intelligence techniques like artificial neural networks (ANNs). For a few years, ANN techniques have been applied with success in control of APF [2] and are very promising in the field. Indeed, the learning capacities of the ANNs allow an online adaptation to every changing parameter of the electrical network, e.g., nonlinear and time-varying loads. Most of these control constraints are quite still very challenging with classic control methods. In this paper, the authors analyze and synthesize most of the ANN strategies already known in order to propose a novel, unified and complete ANNs approach to control APFs. Indeed, the main motivation of this paper is to propose a unified neural approach of the entire adaptive harmonic compensation system. The proposed approach is unified in the sense that it is only based on a single type of ANN: the Adaline neural network [3]. This approach is motivated by a need of simplicity and flexibility in ANNs-based control strategies used in electrical systems, but also to optimize the hardware resources required for the neural algorithmic implementation. Based on a functional decomposition, each computing task of our neural APF is achieved with ANNs. These blocks are an Adaline-based disturbance voltage regulation [4], an Adalinebased harmonic estimation [5]–[7], and neural control schemes of the inverter [8]. New voltage decompositions are developed for the two first blocks and are transferred to the Adalines by fixing their structure and their inputs. The control of the inverter, the third block, uses multilayer perceptrons (MLPs) which can be considered as a combination of several simple unit neurons, i.e., Adalines. As will be shown through numerous simulation and experimental results, the proposed neural APF is better than the conventional APF for the determination and compensation of the harmonic distortions. Moreover, results show excellent behaviors and performances, as well as robustness and usefulness. The

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main advantage of the proposed neural APF is its ability to adapt to time-varying nonlinear loads in real-time. The compensation structure is modular, composed of different blocks of homogeneous ANNs, and can thus be used as a basis for more general architectures, and especially for hardware implementation. The rest of this paper is organized as follows. In Section II, we retrospect the recent APF developments. Our proposed neural strategies are described in Section III. Simulation results and experimental results are presented, respectively, in Sections IV and V. Finally, in Section VI, we conclude our work with discussion and some possible future directions. II. APF FOR HARMONIC COMPENSATION IN ELECTRICAL SYSTEMS A. Active Power Filter Topologies Most of the APF topologies use voltage source converters, which converts a DC voltage into an AC voltage by appropriately gating the power semiconductor switches. The pulsewidth modulated (PWM) technique is the most commonly used today and several topologies where developed based on this principle [9]: series, shunt, series-shunt, or hybrid APFs. Recent research works actually try to improve these APF topologies. In [10], for example, a series APF able to eliminate harmonics, correct unbalanced loads, and generate or absorb reactive power is presented. Based on a combination of a thyristor-binary-compensator and a PWM-insulated gate bipolar transistor (IGBT) APF connected in cascade, the approach is able to respond to many kinds of transient perturbations. APF with a prediction step have been introduced in [11] to extract the sinusoidal active current from the distorted waveform without harmful phase shift, even when the frequency and amplitude alter simultaneously. The proposed structure which combines a low-pass prefilter and an adaptive predictive filter, can be used when the frequency alters rapidly. Interesting combination topologies of series-shunt APFs are proposed in [12] and acceptable filtering characteristics are demonstrated experimentally. Nowadays, among all APF’s topologies, the shunt APF is the most widely used in industrial sites. It can be considered as the most basic configuration of APF, and includes the main critical issues associated with APF control [1], [9], [13]. Consequently, the authors of this paper propose to study specifically different ANN strategies for the shunt APF in order to achieve a unified neural architecture for APF. This work can be easily extended afterward to general topologies of APF, as series or combined shunt/series APF. B. Principle of the Shunt Active Filter In a power system, the voltage (or current) source waveform usually consists of a fundamental component, some harmonic components, and random noise. Among these contents, the fundamental component generally has a significantly dominant proportion. The APF’s rule consists in identifying all the present harmonic components and to separate the fundamental component

Fig. 1. A shunt APF in a power system.

from the other harmonic components which are converted in resulting reference currents. A control strategy uses these reference currents to reinject them in real-time into the utility source with opposite phase through a power circuit, i.e., an inverter and an output filter. This principle is presented in Fig. 1. The architecture of an APF can be decomposed in the following blocks: a first block which allows to recover an equilibrated and balanced voltage system required by some harmonic detection algorithms, a second block which have to identify and to filter the harmonics in order to specify the required reference currents for the control algorithms, i.e., the third block which reinjects the currents via a the APF’s power circuit composed of a inverter and an output filter. While the APF’s first block is traditionally achieved with a phase-locked loop (PLL), the harmonic identification and filtering are based on harmonic detection algorithms such as such as recursive least error squares, least mean squares (LMS), and Kalman filtering. Beyond their simplicity, their drawbacks are non-negligible: they cannot take into account noises and errors, based on optimal models, they require to know the values of the parameters, and they need an incompressible time-delay to convergence. Other alternatives are algorithms generally based on the Fourier theory. The basic assumption, which needs to be satisfied before applying algorithms issued from this theory, is that the signal is stationary or with a constant magnitude. However, in practice, the stationary assumption is normally valid only a certain extent. Another technique, very powerful and that has been very much used, is the Instantaneous Power Theory (IPT) proposed by Akagi [1]. This technique computes the instantaneous active and reactive powers from the measured currents under sinusoidal balanced conditions. Instantaneous active and reactive powers can be decomposed into DC components related to the fundamental frequency and into AC components related the harmonic distortions. A filtering operation is then used to separate the terms produced by the harmonic distortions from the DC components related to the fundamental frequency. The IPT instantaneously only identifies the power properties of equilibrated and balanced systems and specifies the required reference currents for the control strategy. APF’s control strategies are numerous and various. The main control strategies are listed in [13]. Hysteresis band controller method, PID closed loop, and also RST controller are widely used. The control method based on a PID closed loop forces

ABDESLAM et al.: A UNIFIED ARTIFICIAL NEURAL NETWORK ARCHITECTURE FOR ACTIVE POWER FILTERS

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Fig. 2. Overview of the proposed neural APF’s principle.

the line supply currents to be sinusoidal and in phase with the corresponding main voltages. The PID controller keeps the , by manipAPF capacitor voltage according to a reference ulating the amplitude of . The RST controller is the most popular due to its good compromise between its performance and complexity. These methods generally provide satisfactory dynamics performances. For a few years, artificial intelligence techniques were used more and more to control APFs. For instance, ANNs by their learning capabilities allow them to constantly adapt themselves to any changes and thus to be very efficient. C. Artificial Intelligence Techniques for Active Power Filtering The most critical issues associated with conventional APF control is that of finding an algorithm which can obtain an accurate harmonic reference signal for control purpose, particularly in the practical case where the harmonics and the system frequency of a power system are time-varying. This is the reason why adaptive filtering technologies and ANNs-based APF are developed. Some applications are formalized in [2] or in [14] and a recent review of artificial intelligence techniques for the control in power systems is proposed in [15]. The ANNs-based approaches are more and more efficient in active power filtering. Indeed, many works show that ANNs can be used to implement the different parts of an APF. As reported in [16], an Adaline is used to identify the parameters of a discrete signal model of the power system voltage. MLPs are used for harmonic identifying and filtering [17], while feed-forward and recurrent neural networks are used to detect harmonics from distorted wave instead of Fourier transformation and low-pass filter [18]. In both cases, an improvement of the processing speed and a simplification of the harmonic detection algorithm is obtained. An Adaline for online tracking the harmonic components is investigated in [19]. The proposed algorithm can yield markedly better results than most conventional adaptive algorithms for semistationary signals. The objective function used in the proposed algorithm combines a forgetting function and,

hence, the mean error energy can force the system to adjust better to nonstationarity. Another original technique for compensating all voltage disturbances can be found in [20]. Isolation of high-order harmonics is achieved by the combination of a series active filter and parallel passive filter. A genetic algorithm is applied in order to determine the parameters of a RST regulator for a PLL closed loop control to identify the positive sequence of network voltage. This will be used for harmonic identification of the active filters. Moreover, an approach that uses an Adaline is presented in [21] in order to determine the power system frequency. The proposed algorithm shows a high degree of robustness and estimation accuracy over a wide range of frequency changes. All these results show that ANNs can bring efficient solutions to the different steps of active power filtering. III. A NEW NEURAL APF The neural approach we propose is divided in three main blocks previously detailed and shown in Fig. 2. In the first block, Adalines are used to online extract the direct and inverse voltage components from the composite voltage, and thus to determine compliant reference signals, i.e., sinusoidal and equilibrated voltages. Three different neural methods, each based on a specific decomposition of the currents or powers are proposed for the second block which identifies the harmonic currents. In the three methods, the decomposition is implemented with Adalines. The learning algorithm, thus, determines the compensation currents from the identified distortion harmonics. The third block uses ANNs to control the inverter of the APF in order to properly inject the compensation currents. We use different learning schemes [22] based on MLPs to automatically adapt to any changes of the nonlinear load and to the generated harmonics without any prior knowledge. A. Adaline-Based Disturbance Voltage Regulation Basically, APF’s voltage measurement unit is driven by a PLL to recover the balanced voltages from an electrical network

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under severe operating conditions. Based on the IPT, this unit takes as inputs the three-phase voltages through a PLL. Indeed, the IPT is only valid with sinusoidal and balanced voltages and the PLL is then generally used for the extraction of the direct component of the voltage. Furthermore, recent works for improving the PLL have shown some interesting results with the use of improved RST regulators [23]. We propose to use four Adalines as an alternative for online extracting the direct, inverse, and homopolar voltage components from a composite voltage. This neural approach is based on a new voltage decomposition of the unbalanced three-phase system [4]. The learning of the Adalines computes and determines the amplitude and the phase of the direct and inverse voltage components. The homopolar voltage components are then obtained by subtracting the direct and inverse voltage components. The proposed voltage decomposition is introduced thereafter. The objective consists in estimating the amplitudes , , and the phases , , of, respectively, , , and , the direct, inverse, and homopolar voltage components of a threewith the fundamental frequency phase system

Park transform with a rotation of and a to the following equation:

to (4) leads

(5) and thus to (6) Considering , the direct voltage component is then given by (7) From

, one can deduce

(1) if (8) if

(2)

(3)

In the same way, writing (4) in the Park representation after transform and a Park applying successively an leads to transform with a rotation of (9)

The unbalanced voltages of a three-phase system thus, be written

can, and thus to (10) and if (11) if (4)

One can see that (6) and (9) can, respectively, be written with a vector notation as linear relations

with the following Clarke-Park transform parameters: (12) (13) and

The direct, inverse, and homopolar voltage components must be separated and extracted to be in linear relations that can be and , the components of the learned by Adalines. For this, direct voltage and quadrature voltage will be determined in the transform Park representation. Applying successively an

with

(14)

(15)


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Fig. 3. Block diagram of the extraction of the direct and inverse voltage components with four Adalines.

(16)

and (17) Equations (12) and (13) are learned with four Adalines. , , , and are the Adaline weights which will be esti, , , and mated iteratively. The input vectors, need and which are given, respectively, by (8) and (11). The neural structure composed of the four Adalines is represented in Fig. 3. The values of , , , and , respectively, given by (7), (8), (10), and (11), allow to determine the direct and inverse voltage components. The homopolar voltage components will be given by subtracting the direct and inverse voltage components from the measured voltage. Designed to fit the previous mathematical decompositions, the four Adalines are iteratively adapted with a modified LMS learning rule, as follows [3]: if if (18) . is the Adaline with and are, respectively, weight vector at sample time , the input vector and the error at sample time . The error is the current difference between the estimated output of the Adaline and a desired value. is the learning rate parameter and is an appropriate constant to ensure a nonzero low part.

The learning allows an online adaptation to the changing parameters of the electrical network, e.g., nonlinear and time-varying loads. Compliant reference signals, i.e., sinusoidal and equilibrated voltages, can thus be obtained from the proposed neural method, enhancing the performance of the whole harmonic compensation system. Another advantage of this neural method is its ability to online extract the direct, the inverse, and the homopolar voltage components at the same time, while the conventional PLL only returns the direct components. B. Adaline-Based Harmonic Estimation The performances in terms of harmonic compensation strongly depend on the harmonic detection method. Indeed, an efficient control device will not be able to make the sufficient corrections if the harmonic currents are badly identified or with time-varying nonlinear loads. For this reasons, we propose to identify the amplitude of the harmonics of the power system voltage with a neural approach. The simple structure of Adalines allows to physically interpret the weights, and thus makes them very powerful in the field of adaptive filtering [3]. In our harmonic compensation scheme, Adalines are used to identify and to estimate the harmonic components of the currents in the electrical supply network. Each harmonic component of the currents can be estimated with one Adaline’s weight. We use this principle and propose three different techniques. The first method uses the Fourier signal decomposition of the currents measured on each phase [7]. This decomposition can be used directly as inputs of an Adaline. The second one represents an original approach of identification valid for three-phase and single-phase applications [6]. It uses two Adalines for each phase in order to separate the harmonics from the fundamental

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for a constant term corresponding to a bias). Expression (19) of the current can thus be written as a linear combination which can be learned by an Adaline

(21)

Fig. 4. Adaline for harmonic distortion identification with the direct method.

frequency. A Fourier series decomposition of the signal allows to define the neural network inputs. A LMS algorithm carries out the weights training. The facility of use, as well as the parallel unfolding of computations, makes this approach fast and effective. The third method exploits the instantaneous real and imaginary powers technique which is largely used in active filtering systems. As reported in [5], we replaced the low-pass filter by two Adalines. Significant improvements were obtained in simulations and on real applications, but the method is applicable only for the three-phase systems. The three methods which are now detailed were also compared in [7]. 1) The ”Direct” Method: In the ”direct” method [7], the identification and filtering are carried out in the space of the currents (the three-phase space). Each measured current of the three phases of the electrical supply network can be decomposed into Fourier series in the following way:

From this expression, we can define issued from the fundamental harmonic

with the following vectors shown in (22) and (23) at the bottom of the page. is the Adaline weight vector and is the Adaline input vector composed of the cosine and sine components of the harmonics of different orders. We propose to learn (21) with an Adaline by using the modified LMS learning rule given by (18). The estimated fundamental current is then recovered as follows: , where and represent the weights of the Adaline associated to the cosine and sine terms of the fundamental current. The difference between this estimated current and the measured one gives the sum of the different harmonic components and reference currents can . thus be calculated with 2) The Three-Monophase Method: The three-monophase method works directly in the currents space and uses two Adalines to identify the harmonics for each phase. The details can be found in [6]. of the decomposition of the current of (19) be absorbed by the Let us consider the current ). By multiplying nonlinear load on the first phase (for (19), respectively, with and , we obtain the following expressions for harmonics:

(19) , the load current

(20) and the current issued from the harmonics except the fundamental, . In the previous equations, is the phase between current and and the cosine and sine frequency compoload voltage, nents of fundamental current, and the cosine and sine frequency components of the distortion current. is the number of harmonics that must be taken into account. The identification of the harmonic components is done with an Adaline for each phase. As shown in Fig. 4, the Adaline takes cosine and sine terms resulting from the decomposition into Fourier series of the measured current as the inputs (except

(24)

(22) (23)


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(25) In these relations, only terms representing the continuous components are proportional, respectively, to the amplitude of and to the amplitude of the the fundamental active current . Expressions (24) and (25) can fundamental reactive current both be written as a linear combination which can be learned separately by two Adaline neural networks

Fig. 5. The three-monophase uses two Adalines for fundamental current identification.

(26) and with (27)–(29) shown at the bottom of the page. and represent the In the two expressions of (26), weight vectors of two Adalines and is the corresponding input vector composed of the cosine and sine components of the harmonics of different orders. The amplitude of the continuous component of the fundamental active current will be determined by the biais weight of the first Adaline neural network. The continuous component of the reactive current will also be calculated by the biais weight of the second Adaline neural network. The reconstituted fundaand mental current is obtained by multiplying, respectively, with and : . The harmonics current of each phase is then given by . The complete scheme of the estimation of the reference current, , for one phase is represented in Fig. 5.

3) The Active and Reactive Powers Method: Converting the measured currents into the active and reactive powers is conventionally achieved by the direct Concordia transform (see block 1 in Fig. 6). We propose to use this transform in a new method, the active and reactive powers method, which is represented in Fig. 6 and decomposes the powers as shown in (30) and (31) [5] at the bottom of the page. Both powers given in (30) and (31) can be written in a discrete that linear expression in vectorial notation of the form can be learned with an Adaline. The Adaline for learning (30) uses the following weight vector and input vector shown in (32) and (33) at the bottom of the page. A similar decomposition is used to learn (31). Two Adalines are, thus, employed to identify and separate the alternative comand from the continuous components and ponents in (30) and in (31). This is block 2 in Fig. 6. The biases of the two neural networks identify the continuous components

(27) (28) (29)

(30)

(31)

(32) (33)

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Fig. 6. Adaline for harmonic distortion identification in the active and reactive powers method.

TABLE I COMPARISON OF THE DIFFERENT COMPENSATION TECHNIQUES

of the active and reactive powers. An inverse Concordia transform enables then to find out the harmonic currents from the power alternative components (see block 3 in Fig. 6). The refis then deduced from the inverse Concordia erence current transform. This method should give better results than the direct method due to the precision to obtain the continuous components. 4) Comparison of the Three Methods: To evaluate the three proposed identification methods, a practical case which is representative of the most common power quality environment was simulated in Matlab–Simulink. In the considered power system, the three-phase source has the following characteristics: , , , and the frequency is . In order to create harmonics distortions, a nonlinear load (a Graetz bridge with RC branches and six valve functions ) has been introduced with the following with an angle , and . parameters: 100 kVA, In order to compare the neural identification methods, we kept the same control device for the three cases. The control of the inverter is achieved with a traditional RST control scheme to only enlighten the performances of the neural identification methods. The APF equipment is connected in parallel to the voltage supply. The total harmonic distortion (THD) is measured to show the performances of each technique. We also compared these results with the results obtained with a conventional

low-pass filter [7]. The steady-state performances of all methods are given in Table I. According to Table I, one can see that the three identification techniques using Adalines give good results compared with the low-pass filter method. The direct method is the simplest in term of implementation because it does not require any PLL and current transformations but the compensation performances are not the best. On the other hand, the THD is reduced to 0.79% with the three-monophase method which does not require a PLL but needs current transformations. To evaluate the transient behavior of each method, the nonis linear load is changed at time 0.28 s. The global resistance . The performances of each method then changed from 2 to are evaluated with the corresponding THD through time and it can be deduced that the active and reactive powers method is the faster and accurate one to compensate for harmonic distortions when the nonlinear load is online changed. The currents of the power system is shown in Fig. 7 with the active and reactive powers method to compensate for the harmonics distortions. The figure shows the current waveform before [Fig. 7(a)] and after [Fig. 7(c)] the compensation, and shows the current resulting from the harmonics distortions [Fig. 7(b)], while the nonlinear load is online changed at time 0.28 s. As can be seen, according to its learning capabilities, the neural identification method, i.e., the active and reactive


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Fig. 7. Dynamic response of the neural APF with the active and reactive powers method associated to a RST control scheme when the nonlinear load is simulated and changed at time t = 0:18 s. [The time is expressed in seconds (s) and the currents in amps (A)]. (a) Three-phase original current waveforms. b) Three-phase reference current waveforms. c) Three-phase compensated current waveforms.

powers method, adapts itself to the changing nonlinear load by keeping the source current waveforms perfectly sinusoidal. The methods are also able to compensate for the reactive power. Indeed, if the harmonic compensation is done with the for the active and reactive powers alternative powers . If the two method, the power factor correction is done with objectives are wanted simultaneously, both terms should be used to compute the reference currents. On the other side, the harfor the direct monic compensation is done with and three-monophase methods and the power factor is compen. From Table I, we can see that the power sated with factor is corrected and close to unity with the neural methods, i.e., over 0.99 instead of 0.987 with the conventional method (for a non corrected value of 0.941). Each method is different because it is based on a specific decomposition of the current signals. The direct method simply uses an Adaline to directly estimate the fundamental harmonic from the measured current. The three-monophase method

uses two Adalines for each phase to estimate the fundamental harmonic from the measured current. The active and reactive powers method uses an Adaline to estimate the fundamental harmonic from the active and reactive powers. One can see that the neural compensation methods are more efficient than other conventional methods to compensate for the harmonic distortions and to correct the power factor. C. Neuro-Control Schemes After having identified the distortion harmonics, the resulting reference currents have to be injected phase-opposite in the electrical power systems. This is generally done with a inverter and a supply filter implemented by analog circuits. Different algorithm strategies can be applied to control the inverter and inject suitable signals in regard to reference signals estimated previously. Hysteresis band controller, PID controller, and RST controller were widely used in recent literature [24].

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TABLE II COMPARISON OF DIFFERENT CONTROL TECHNIQUES

We propose here to apply control strategies based on ANNs, where multilayer and recurrent networks interconnected in novel configurations were introduced in a unified mode. This method has been reported in a previous work [22]. The neuro-controller achieved by this method is an unconventional adaptive controller that constantly improves its performances through an online learning process [25]. Its main objective is to learn and to identify a traditional controller. We thus evaluate and compare three different strategies [8]: a direct identification scheme, an inverse control scheme, and finally, a direct-inverse neuro-control scheme. These control strategies and their principles are briefly presented thereafter. 1) Direct Neuro-Control Scheme: This neuro-control scheme is based on an ANN that learns a conventional RST controller with acceptable performances. This ANN is then simply cascaded with the controlled plant. The controller ANN acts directly as the controller in this configuration. The RST is first learned and then replaced by the ANN. At the end of the learning phase, the ANN is an exact copy of the RST controller. This learning scheme gives an efficient controller and overcomes the conventional RST’s limits. In a direct control scheme, the goal is to directly minimize the system output error, which is the difference between the desired and the current output of the plant, i.e., . The neuro-controller is implemented by a MLP which can be considered as several Adalines connected amongst themselves. The input of this neuro-controller are the outputs of the RST , and past estimation errors, i.e., controller at time and the difference between the RST controller output and the neural , , and . The network network output: is trained by the standard backpropagation learning algorithm [25], and the estimated outputs are the control signals for the inverter and the passive filter. 2) Inverse Neuro-Control Scheme: While the direct control scheme is based on offline learning, the inverse control scheme allows to use online learning in order to compensate for the failing and to reproduce the qualities of the RST controller. The inverse control scheme is also able to take into account every change of the nonlinear load. The ANN is used to learn the RST controller. The neuro-controller, a MLP, computes the control signal based on the online learning. The MLP is composed of four inputs, the current output of the network and the current and delayed error, and it has one output which is the control signal of the inverter. The training is usually done by iterative updating of the weights in order to minimize a mean-squared error function. The learning of the network weights uses the error signal

which is the difference between the desired and actual output . values of MLP: 3) Direct-Inverse Neuro-Control Scheme: The direct-inverse control utilizes the plant model and its dynamics. The dynamics of the plant is first obtained by using an identifier neural network (INN). A controller neural network (CNN), an exact copy of the INN, delivers directly a control action. Applied to the control of the inverter, the INN estimates the system parame, the Jacobian of the plant. The ters, as well as CNN drives the inverter by using the system dynamics Jacobian of the process. The INN estimates and provides the current to inject in the power system based on the control signal delayed , , , and . Since a several time neuron computes a weighted sum of its inputs, the connections of the neural network with its hidden layer can be seen as a coordinate transformation. For each point of the input space, the network computes its output value from the transformed coor. dinates: This estimated current is injected in the power system through the inverter and the passive filter and is learned with the error . The CNN estimates the control signal of the inverter. Its inputs are the errors , , , and at different successive time step. This error is calculated by comparing the reference current to the current to inject in the power system: . The learning of both networks is governed by the standard backpropagation algorithm and basically demonstrated excellent results. The scheme cannot be used if the plant inverse does not exist. It has the advantage that it does not require to separate the training of the two ANNs. The scheme works online, but it is also possible to first have an initial CNN resulting from offline learning. Online learning can then be used to fine-tune. 4) Comparison of the Three Neuro-Controllers: Computer simulation results are now presented to validate the performance of the neuro-control approaches. The power system is simulated with the same parameter as in Section III-B4, respecting thus industrial operating conditions. The neural compensation approaches are also compared with conventional approaches, i.e., a PID regulator and a RST controller. To properly compare the different neuro-control schemes, the same identification and filtering approach is used: the active and reactive powers method. The THD is measured and given in Table II to show the performance of each method. The size of the ANNs necessary to the different neuro-control schemes is also mentioned to give an approximate idea of their computer and algorithmic costs.


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TABLE III COMPARISON OF THE PERFORMANCES OF THE NEURAL APF UNDER SIMULATED OPERATING CONDITIONS

Table II shows that the THD parameter is reduced from 24.2% to 8% with a conventional APF, i.e., implemented with a low-pass filter and a PID regulator. Replacing the PID regulator by a RST controller reduces the THD parameter to 1.2%. The THD parameter is reduced to 0.84% after the active and reactive powers method is applied for the harmonic filtering. If the direct neuro-controller has no better performance than the RST controller (the THD parameter is 0.92%), the inverse and direct-inverse neuro-control schemes associated to the active and reactive powers filtering method reduce the THD parameter, respectively, to 0.83% and to 0.82%. Therefore, one can see that the neural compensation approaches are efficient. There is a better harmonic compensation with the neural network approaches than with a conventional APF. If the speed of convergence of all the neuro-control schemes is less than 50 ms, the results obtained with the inverse and the direct-inverse approach, are definitely better than the results with the other methods. Indeed, these two neuro-control schemes authorize online training during the inverter control holding thus in account of the changes of the process at every time, and gives very acceptable results. The performances of the inverse and the direct-inverse approaches are practically similar. But in regard to the performance and the number of neurons necessary for these two approaches (see Table II), we will retain the inverse neuro-control scheme because of its simplicity and low hardware requirement. In addition to the performances improvement, our main motivation is the choice of a neural network approach tending toward a “full neuromimetic” control strategy with respect to real-time constraint. That will lead to a structure of strongly homogeneous processing element leading itself favorably to a hardware implementation. Simulation results reveal that the suggested neuro-control scheme has good performance and is easily feasible. IV. SIMULATION RESULTS The effectiveness of our proposed neural APF is first verified through computer simulations. In this section, the performance of our neural APF is evaluated in terms of accuracy, robustness, speed of convergence, and computational complexity. Thus, we perform some appropriate operations in accordance with industrial cases, as variations of nonlinear load, using different values

, but also applying various paramof resistance of the load eters of the electrical power source, i.e., voltage amplitude and power supply. The AC main, the load, and the neural harmonic compensator are implemented with Matlab–Simulink, which allows real-time visualization, adjustment, and control of all the parameters. The configuration and parameters of the simulated power system are the same as those given in Section III-B4 which can be considered as representative of a real-world environment. The basic circuit used for the nonlinear load is a Graetz bridge consisting and of six valve functions and a RC circuit with . We propose to compare Adaline-based neuro-techniques introduced in Section III for disturbance voltage regulation and harmonic estimation. In case of neuro-control schemes, we chose to apply only the inverse neuro-control scheme because of its benefits in terms of low computational costs. In the first simulation, we evaluate the proposed neural harmonic compensator with different values of the parameters of the power system. In the second set of simulations, we study its transient behavior, in particular, in the case of varying the charge online. A. Steady-State Performance To estimate performances of the neural APF in steady-state model, we perform several simulations in different operating conditions, i.e., different power source parameters and nonlinear loads. Moreover, we compare our neural APF model with the three different identification methods developed previously. These simulation results are finally compared with conventional harmonic compensation methods. The three identification methods with the inverse neuro-control scheme were used and compared with the conventional method with a low-pass filter and a RST controller. In the simulations, the learning rate are kept constant for the harmonic detection , 0.01, and 0.001 for, respectively, techniques, i.e., the three-monophase method, the active and reactive powers method, and the direct method. The learning rate for the inverse neuro-control scheme is 0.002. All simulation results are given in Table III, where the THD on the source side must be relativised by the THD on the load side (approximately 31%). These results confirm the adequacy

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Fig. 8. Dynamic performance (THD) of the neural compensation scheme with different identification method and an inverse neuro-control scheme when the nonlinear load is changed at time t = 0:28 s. [The time is expressed in seconds (s) and the THD in percent (%)].

and the superiority of the proposed neural approach over conventional methods whatever the conditions. The best performances are obtained with the active and reactive powers method, for which the THD is in some cases lower than 1%. The resulting current waveforms are, thus, compensated with a high accuracy. The speed of convergence of the neuro-control is approximately 50 ms, 25 ms for the disturbance voltage regulation, and 4, 11, and 45 ms for the harmonic detection algorithms, respectively, the three-monophase method, the active and reactive powers method, and the direct method. The speed of convergence of the whole neural APF does not exceed 60 ms which is acceptable in many practical applications. B. Effect of Varying the Charge Online Simulations with an online varying load, i.e., a sudden change of the nonlinear load, are proposed in order to evaluate the dynamic response of the proposed techniques. The amplitude of the harmonic components are modulated by changing of value from 1.6 to at time s. The response of the of neural compensating approach to the change of the nonlinear load is shown in Fig. 8. The corresponding THD is shown in this figure with the three proposed identification methods and are compared with the conventional approach composed of a low-pass filter and a RST controller. The performances of the neural methods are found to be very accurate and 0.05 s. After the sudden change of the nonlinear load, the THD parameter is kept within 0.005% with the active and reactive powers method. One should notice that the active and reactive powers method requires an equilibrated and balanced voltage system. We used the proposed neural method to online extract the direct, inverse, and homopolar voltage components. Many simulations to compare this neural approach to a conventional PLL were addressed. All the results show that this method is very fast and efficient. All theses simulations clearly show that the neural approach is faster and more precise then the conventional PLL. Indeed, the online estimation of the direct voltage component is done after

Fig. 9. Dynamic response of the neural method to identify the parameters of the voltage components when the nonlinear load is changed at t = 0:23 s.

0.025 s with the neural strategy against 0.045 s with the conventional PLL. These times depend on the amplitude of the changes but here they were measured for changes greater than those in industrial power systems. An example is given in Fig. 9, where s. This figure the nonlinear load is changed at time shows that is always correctly estimated, as well as , , and . C. Discussion Static and dynamic tests show that the performance of the neural strategies is always better than that of the conventional method, i.e., a low-pass filter and a RST regulator. Indeed, all the simulation results clearly demonstrate the advantage of using ANNs in an APF scheme. By their online learning capabilities, ANNs are able to improve themselves and to take into account any changes like variations of the nonlinear load. The direct method and the three-monophase method are simple and efficient, but the active and reactive powers method has the best performance. This method, which requires only two Adalines, needs a voltage component extractor contrary to the two other methods. The active and reactive powers method must, thus, be associated to the proposed neural method for online extracting the voltage components which requires four Adalines. On the other side, the direct method and the three-monophase method are, respectively, based on three and six Adalines. Considering the computational costs and the performance of each neural method, the active and reactive powers method is the method that presents the best compromise. Other computer simulation results indicate that the neural approaches are able to handle with any other fundamental frequency. The proposed decomposition for the three neural methods and also for the proposed voltage component extractor can be updated for any fundamental frequency of the power system. Furthermore, these decompositions allow to compensate or suppress harmonic components individually. This is achieved by choosing the appropriate inputs of the Adaline neural networks used for identifying and filtering the harmonic contents.


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TABLE IV EXPERIMENT RESULTS OF THE NEURAL APF WITH THE ACTIVE AND REACTIVE POWERS METHOD AND THE ONLINE EXTRACTION OF THE VOLTAGE COMPONENTS WITH DIFFERENT CONTROL SCHEMES (AMPLITUDE OF THE MOST SIGNIFICANT HARMONICS IN % COMPARED WITH THE AMPLITUDE OF THE FUNDAMENTAL HARMONIC)

In addition to being able to adapt itself to time-varying parameters, the neural method for the online extraction of the voltage components is able to directly extract all the voltage components, i.e., the direct, the inverse, and the homopolar components, which is not possible with a conventional PLL. V. EXPERIMENTAL RESULTS A. Real-Time Implementation Experimental results are now given to illustrate the APF performance based on the proposed neural strategies. The effectiveness of the proposed neural harmonic compensation schemes is verified with a DS 1104 dSPACE board and an experimental setup of 5 VA. The dSPACE board is based on a Power PC 603e processor and a TMS320C31 DSP at 40 MHz. The power system is a three-phase power supply with a low-voltage system and a nonlinear load which is a Graetz bridge of six valve functions and a RL circuit with a power variator. The load impedance and mH. This experis composed of imental platform allows to reproduce industrial conditions. In s. the all experiments, the sample period is The APF power circuit is an IGBT-inverter with a voltage structure associated to a first-order output filter. The PWM uses a frequency of 10 kHz. A complete neural APF has been implemented to online eliminate the harmonics generated by the nonlinear load. The different neural approaches, i.e., the direct method, the three-monophase method, and the active and reactive powers method with the direct and inverse voltage components neural extractor, have thus been implemented. The control is achieved alternatively with the inverse or the direct-inverse neuro-control scheme. We first evaluate the steady-state behavior of the neural APF, and then its dynamics, i.e., its response to any changes of the nonlinear load. B. Steady-State Performance The following experiments are done in order to evaluate the proposed neural APF in a real-world application. Different operating conditions are used and the neural APF is compared with the popular and conventional technique that is based an a low-pass filter and a RST regulator. Results are presented in Table IV, which shows the resulting amplitude of the most significant harmonics compared with the amplitude of the fundamental harmonic after compensation. This performance proves and confirms the superiority of the proposed neural approach over conventional methods. The active and reactive powers method is alternatively used with

Fig. 10. Steady-state experiment results of the proposed active and reactive powers method associated to an inverse neuro-control scheme (for the first phase).

different neuro-control schemes. The best performances are obtained with the direct-inverse neuro-control scheme where all the amplitude of the high-order harmonics are within 3.9% from the amplitude of the fundamental harmonic. For this neural APF, the THD is in some cases within 1% and is always less than 1.5%. The currents for this neural APF are represented in Fig. 10. As can be seen, the neural APF is generating the harmonic currents to keep the source current sinusoidal and the current waveforms are compensated with a high accuracy. C. Effect of Varying the Charge Online Dynamic conditions are now investigated to evaluate the transient behavior of each method. We change the nonlinear load at time 0.96 s. The global resistance is then changed from to . This is a particularly severe dynamic test case because, due to the change in the nonlinear load, all the harmonic contents will change. The performances of each neural method are evaluated by considering the THD through time. We can see that the maximum THD with the neural approaches is in the order of 0.4%, which is less than 1%. The experiments also show that when the nonlinear load is online changed, the active and reactive powers method is the faster and accurate one to compensate for the harmonic distortions introduced by the nonlinear load. Although the need of a voltage components extractor, this neural scheme is simple and can be easily implemented using a microprocessor for real-time compensation of harmonics in power systems. The results are shown in Fig. 11 with the active and reactive powers method associated to an inverse neuro-control scheme.

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Fig. 11. Dynamic experiment response of the neural APF with the active and reactive powers method associated to an inverse neuro-control scheme when the nonlinear load is changed at time t = 0:96 s for the first phase.

The dynamic response of the neural compensation scheme is represented for one phase. As can be seen, the neural APF compensates the distortion harmonics with a high accuracy even while the nonlinear load is changed. It adapts itself and generates the appropriate harmonic currents to keep the source current waveforms sinusoidal. D. Discussion The experiment results show that the neural compensation techniques are very efficient and better than the conventional APF for the determination of the harmonic components and for the reduction of harmonic distortions. Indeed, ANNs can extract the direct and indirect voltage components, identify individually every harmonic component, solve the variation problem involving the magnitude of harmonics, and control the APF’s power circuit. Their learning capabilities allow them to constantly adapt themselves to any changing parameters with flexibility, i.e., the convergence depends on the learning rate. Consequently, an adaptive learning rate can provide faster convergence and enhance sensitivity in tracking the harmonic components. A learning rate between 0.1 and 1 leads to unstable Adaline learning schemes. Sensitivity and fast learning is obtained with inferior but close to 0.1. In this case, the neural APF is also able to compensate for the noises introduced by the first-order output filter. Thus, the experiments are done with more severe operating conditions than the simulations. However, the results allow concluding that the active and reactive powers method is better than the conventional harmonic detection algorithm and the direct and the three-monophase method. At the same time, it was shown that the direct-inverse neuro-control scheme is better than the two other neuro-control schemes. In the direct-inverse control scheme, two neural networks are used for estimating the parameters and the Jacobian of the process. Without any prior knowledge, this neuro-control

approach online learns and controls the APF’s converter and its dynamics. At last, the benefit of the voltage component extractor is clearly demonstrated: the voltage components, the direct, inverse and homopolar components can be directly extracted from the measured currents. One should notice that this is not the case with the PLL traditionally used with unbalanced three-phase power systems. Experiments with selective harmonic distortion compensation have also been conducted. For example, a practical case with a current polluted with important harmonics of rank 5 and 7 has been considered, i.e., from, respectively, 24% and 10% of the amplitude of the fundamental current. We separately compensate for harmonics of rank 5 and 7. In the two cases, all the neural methods demonstrate their efficiency. Indeed, the amplitudes of these harmonics are reduced to less than 2% of the amplitude of the fundamental current. The neural compensation schemes can thus be considered as efficient in selective harmonic compensation tasks. Finally, experiments with different fundamental frequencies have been conducted. These allow us to show that the proposed techniques are also able to compensate for the harmonics with different fundamental frequencies by updating the currents or powers decomposition for the Adalines. For example, practical cases with the active and reactive powers method and direct-inverse neuro-control scheme leads to harmonics reduced to 3.9% Hz and to of the amplitude of the current associated to Hz. 4% of the amplitude of the current associated to The performances with different fundamental frequencies can thus be considered as quite similar. The filtering neural methods and the neuro-control schemes are based on Adalines. This type of ANN is efficient with a simple learning algorithm. The convergence conditions of Adalines are clearly known, and their structure is adequate for online learning in a real-time implementation. The proposed architecture of the neural APF is thus unified in the sense that only Adalines are involved in filtering, as well as in the control scheme or in the voltage component extraction. VI. CONCLUSION In this paper, an APF system based on ANNs is proposed. This neural APF is able to eliminate the harmonics with a good dynamic response even if the system is unbalanced. This is in part due to the proposed neural disturbance voltage regulation. Indeed, the learning capabilities of ANNs applied to the APF allow an online adaptation to every changing parameters of the electrical network, e.g., nonlinear and time-varying loads. We thus developed a neural method for the online extraction of the direct and inverse voltage components to recover a balanced and equilibrated voltage system and three different methods for harmonic filtering. We also used three different learning schemes to control the inverter to inject the reference currents in the power system. Each proposed neural method is based on original mathematical decompositions of either the currents or the instantaneous powers. The neural APF is composed of three parts that work independently, making it simple and more reliable. Each part, the extraction of the voltage components, the filtering of the harmonics, and the control of the APF’s power circuit are all based


on a unique type of ANN: the Adaline. This leads to a unified neural approach with increased efficiency and improved performance. In addition to the performance improvement, our main motivation in the choice of a neural network approach remains of tending toward a “full neuromimetic” strategy. That will lead to a structure of strongly homogeneous processing elements leading themselves favorably to a hardware implementation. The main advantage of the proposed technique is its ability to adapt to varying loads in real-time. The compensation structure is modular, composed of different blocks of homogeneous neural networks, and can thus be used as basis for more general architectures, and especially for hardware implementation. The analytical expectation has been verified by extensive simulation results using Matlab–Simulink. The results obtained show an excellent behavior under both steady-state and transient situations. Moreover, the robustness and efficiency has been evaluated by experiments. The obtained results confirm the adequacy of the proposed neural approach to identify the distortion harmonics with high accuracy, and thus provide the power system with the appropriate compensating currents. Fast and more precise then the conventional APFs, the neural strategies for the APF learn and adapt themselves online to restore with a high accuracy the current waveforms disturbed by time-varying nonlinear loads. The proposed techniques are also able to eliminate only specific harmonics and are well suited for power systems with any fundamental frequency. Our perspectives concern the improvement of the APF to take into account at a earliest stage the deviations of the fundamental frequency.

REFERENCES [1] H. Akagi, “New trends in active filters for power conditioning,” IEEE Trans. Ind. Appl., vol. 32, no. 6, pp. 1312–1322, Nov.–Dec. 1996. [2] L. L. Lai, Intelligent System Applications in Power Engineering: Evolutionary Programming and Neural Networks. New York: Wiley, 1998. [3] B. Widrow and E. Walach, Adaptive Inverse Control, ser. Information and System Sciences Series. Upper Saddle River, NJ: Prentice-Hall, 1996. [4] D.O. Abdeslam, D. Flieller, P. Wira, and J. Mercklé, “Adaline neural networks for online extracting the direct, inverse and homopolar voltage components from a composite voltage,” in Proc. 31st Annu. Conf. IEEE Ind. Electron. Soc., Raleigh, NC, 2005, pp. 1000–1005. [5] D.O. Abdeslam, J. Mercklé, R. Ngwanyi, and Y.-A Chapuis, “Artificial neural networks for harmonic estimation in low-voltage power systems,” in Proc. 4th Int. ICSC Symp. Eng. Intell. Syst., Island of Madeira, Portugal, 2004. [6] D.O. Abdeslam, J. Mercklé, and P. Wira, “Adaline-based estimation of power harmonics,” in Proc. 13th Eur. Symp. Artif. Neural Netw., Bruges, Belgium, 2005, pp. 571–576. [7] D.O. Abdeslam, J. Mercklé, P. Wira, and D. Flieller, “Harmonic identification based on ANN. A comparative study,” in Proc. 9th Int. Conf. Eng. Appl. Neural Netw., Lille, France, 2005, pp. 179–186. [8] D.O. Abdeslam, P. Wira, J. Mercklé, and Y.-A Chapuis, “A neural approach for the control of an active power filter,” in Proc. 5th Int. Power Electron. Conf., Niigata, Japan, 2005, pp. 923–929. [9] L. A. Morán and J. W. Dixon, “Active filters,” in Power Electronics Handbook, M. Rashid, Ed. New York: Academic, 2001, pp. 829–841. [10] J. W. Dixon, G. Venegas, and L. A. Morán, “A series active power filter based on a sinusoidal current-controlled voltage-source inverter,” IEEE Trans. Ind. Electron., vol. 44, no. 5, pp. 612–620, Oct. 1997. [11] S. Valiviita and S. Ovaska, “Delayless method to generate current reference for active filters,” IEEE Trans. Ind. Electron., vol. 45, no. 4, pp. 559–567, Aug. 1998.

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[12] F. Peng, H. Akagi, and A. Nabae, “A new approach to harmonic compensation in power systems; A combined system of shunt passive and series active filters,” IEEE Trans. Ind. Appl., vol. 26, no. 6, pp. 983–990, Nov.–Dec. 1990. [13] R. F. Pinheiro, C. B. Jacobina, A. M. N. Lima, and E. R. C. da Silva, “A revision of the state of the art in active filters,” in Proc. 5th Brazilian Power Electron. Conf., Foz do Iguaçu, Brazil, 1999, pp. 857–862. [14] M. N. Cirstea, A. Dinu, M. McCormick, and J. G. Khor, Neural and Fuzzy Logic Control of Drives and Power Systems. Oxford, U.K.: Newnes, 2002. [15] R. Bansal, T. Bhatti, and D. Kothari, “Artificial intelligence techniques for reactive power/voltage control in power systems: A review,” Int. J. Power and Energy Syst., vol. 23, no. 2, pp. 81–89, 2003. [16] J. R. Vázquez, P. Salmerón, J. Prieto, and A. Pérez, “A practical implementation of a three-phase active power line conditioner with ANNs technology,” in Proc. 28th Annu. Conf. IEEE Ind. Electron. Soc., Sevilla, Spain, 2002, vol. 1, pp. 739–744. [17] J. R. Vázquez, P. Salmerón, and F. Alcantara, “Neural networks application to control an active power filter,” in Proc. 9th Eur. Conf. Power Electron. Appl., Graz, Austria, 2001. [18] F. Temurtas, R. Gunturkun, N. Yumusaka, and H. Temurtas, “Harmonic detection using feed forward and recurrent neural networks for active filters,” Electric Power Syst. Res., vol. 72, pp. 33–40, 2004. [19] Y. Wang, J. Gu, and C. Chen, “An improved Adaline algorithm for online tracking of harmonic components,” Int. J. Power and Energy Syst., vol. 23, no. 2, pp. 117–127, 2003. [20] J. Phinney and D. J. Perreault, “Filters with active tuning for power applications,” IEEE Trans. Power Electron., vol. 18, no. 2, pp. 636–647, Mar. 2003. [21] P. Dash, D. Swain, A. Routray, and A. Liew, “An adaptive neural network approach for the estimation of power system frequency,” Electric Power Syst. Res., vol. 41, pp. 203–210, 1997. [22] K. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Netw., vol. 1, no. 1, pp. 4–27, Mar. 1990. [23] M. A. E. Alali, S. Saadate, Y. Chapuis, and F. Braun, “Advanced corrector with FPGA-based PLL to improve performance of series active filter compensating all voltage disturbances,” in Proc. Eur. Conf. Power Electron. Appl., Graz, Austria, 2001. [24] M. A. E. Alali, Y.-A. Chapuis, S. Saadate, and F. Braun, “An advanced common control method for shunt and series active compensators used in power quality improvement,” Proc. Inst. Elect. Eng. -Electric Power Appl., vol. 151, no. 6, pp. 658–665, 2004. [25] P. Vas, Artificial-intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-Neural and Genetic-AlgorithmBased Techniques, ser. Mographs in Electrical and Electronic Engineering. Oxford, U.K.: Oxford Univ. Press, 1999.

Djaffar Ould Abdeslam was born in Tizi Ouzou, Algeria, on April 20, 1976. He received the M.Sc. degree in electrical engineering from the University of Franche-Comté, Besançon, France, in 2002. He is now working towards the Ph.D. degree at the University of Haute Alsace, Mulhouse, France. His work concerns artificial neural networks applied to power active filters and power electronics.

Patrice Wira received the M.Sc. degree and the Ph.D. degree in electrical engineering from the University of Haute Alsace, Mulhouse, France, in 1997 and 2002, respectively. Since 2002, he has been with the MIPS Laboratory, University of Haute Alsace, where he is an Associate Professor. His current research interests are artificial neural networks, adaptive control systems, and neuro-control applied to robotics and to visual servoing. His research works also include artificial neural networks applied to harmonic compensation and active power filters.

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Jean Mercklé received the M.Sc. and Ph.D. degrees in electrical engineering from University Nancy I, Nancy, France, in 1982 and 1988, respectively. In 1988, he joined the MIPS Laboratory, University of Haute Alsace, Mulhouse, France, where he participated in several adaptive signal processing projects. From 1991 to 1993, he was with the Department of Electrical and Computer Engineering, University of California at San Diego, contributing to a 3-D optoelectronic neural architecture with efficient learning. He is currently a Professor of Electrical and Computer Engineering. His research interests include adaptive neural computation with application to power electronic systems control and digital hardware implementation.

Damien Flieller was born in Epernay, France, on October 15, 1966. He received the M.Sc. degree in electrical engineering from the Ecole Normale Supérieure de Cachan, France, in 1988 and the Ph.D. degree in electrical engineering from the University of Paris, Paris, France, in 1995. He was first a Researcher in the Laboratoire d’Electricité, Signaux et Robotique, ESA CNRS (1990–1995). Since 1995, he has been an Associate Professor with the Department of Electrical Engineering, Institut National des Sciences Appliquées

(INSA), Strasbourg, France. He is now Director of the ERGE Laboratory, INSA. His research interest are in the field of modeling and control of synchronous motors, switched reluctance motors, active filter, and induction heating DC-AC converters.

Yves-André Chapuis received the M.Sc. degree in electrical engineering from the Institut National Polytechnique de Toulouse (INPT), Toulouse, France, in 1990 and the Ph.D. degree in electrical engineering from the Institut National Polytechnique de Grenoble (INPG), Grenoble, France, in 1995. From 1995 to 1996, he was a Research and Development Engineer with GEC Alsthom (railway section), Villeurbanne, France. From 1996 to 2002, he was an Associated Professor with the University Louis Pasteur (ULP), Strasbourg, France, where he investigated in the field of control systems and VLSI and FPGA implementation for electrical systems (motor drive and power supply compensator). Since 2002, he has been a Researcher at the Center National de la Recherche Scientique (CNRS) in the LIMMS/CNRS-IIS (UMI 2820) a joint France and Japan Laboratory of the Institute of Industrial Science, University of Tokyo, Tokyo, Japan, where he is a Visiting Researcher of the Fujita Laboratory. His main research concerns distributed MEMS and NEMS, decentralized control system, intelligent microsystems, and IC/MEMS co-integration technology.