Neural-Network-Based Maximum-Power-Point Tracking of Coupled-Inductor Interleaved-Boost-Converter-Supplied. PV system using a fuzzy controller.

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1 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 4, AUGUST Neural-Network-Based Maximum-Power-Point Tracking of Coupled-Inductor Interleaved-Boost-Converter-Supplied PV System Using Fuzzy Controller Mummadi Veerachary, Member, IEEE, Tomonobu Senjyu, Member, IEEE, and Katsumi Uezato Abstract The photovoltaic (PV) generator exhibits a nonlinear characteristic and its maximum power (MP) point varies with solar insolation. In this paper, a feedforward MP-point tracking scheme is developed for the coupled-inductor interleaved-boost-converter-fed PV system using a fuzzy controller. The proposed converter has lower switch current stress and improved efficiency over the noncoupled converter system. For a given solar insolation, the tracking algorithm changes the duty ratio of the converter such that the solar cell array voltage equals the voltage corresponding to the MP point. This is done by the feedforward loop, which generates an error signal by comparing the instantaneous array voltage and reference voltage corresponding to the MP point. Depending on the error and change of error signals, the fuzzy controller generates a control signal for the pulsewidth-modulation generator which in turn adjusts the duty ratio of the converter. The reference voltage corresponding to the MP point for the feedforward loop is obtained by an offline trained neural network. Experimental data are used for offline training of the neural network, which employs a backpropagation algorithm. The proposed peak power tracking effectiveness is demonstrated through simulation and experimental results. Tracking performance of the proposed controller is also compared with the conventional proportional-plus-integral-controller-based system. These studies reveal that the fuzzy controller results in better tracking performance. Index Terms Coupled-inductor interleaved boost converter, feedforward loop, fuzzy controller, maximum power (MP) operation, neural network, proportional plus integral controller, solar cell array (SCA). I. INTRODUCTION THE rapid trend of industrialization of nations and increased interest in environmental issues led recently to the exploration of the use of renewable forms such as solar energy [1] [3]. Photovoltaic (PV) generation is gaining increased importance as a renewable source due to its advantages like absence of fuel cost, little maintenance, no noise and wear due to absence of moving parts etc. In particular, energy conversion from a solar cell array Manuscript received December 12, 2001; revised August 21, Abstract published on the Internet May 26, M. Veerachary was with the Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Nakagami, Okinawa , Japan. He is now with the Department of Electrical Engineering, Indian Institute of Technology, New Delhi , India. T. Senjyu and K. Uezato are with the Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Nakagami, Okinawa , Japan. Digital Object Identifier /TIE (SCA) received considerable attention in the last two decades. Since the PV generator exhibits a nonlinear characteristic, its maximum power (MP) point varies with the solar insolation and temperature. At a particular solar insolation, there is a unique operating point of the PV generator at which its power output is maximum. Therefore, for maximum utilization efficiency, it is necessary to match the PV generator to the load such that the equilibrium operating point coincides with the MP point of the PV source. However, since the MP point varies with insolation and seasons, it is difficult to maintain MP operation at all solar insolations without changes in the system parameters. To overcome this problem, use of an intermediate dc dc converter is proposed [1] [4], which continuously adjusts the voltage, current levels, and matches the PV source to the load. Different peak power tracking schemes have been proposed by using different control strategies [5]. Array-voltage-based maximum-power-point tracking (MPPT) using dc dc converters has been reported [6] [8]. This method of MP tracking has the advantages of straightforward array voltage measurement, no need of current sensors (which introduces losses and complexity in the system), etc. An interleaved dual boost converter scheme is proposed by the authors for the array-voltage-based MP extraction [9] by controlling the converter in an interleaved fashion. Advantages of the present converter system [10] are: 1) ripple cancellation both in the input and output waveforms to the maximum extent,; 2) lower value of ripple amplitude, high ripple frequency in the resulting input and output waveforms; and 3) reduced electromagnetic interference because of low ripple amplitude of the SCA current. Although the interleaving technique increases the number of components, the actual increase of cost may not be significant. This is because when a large number of converter cells are used they can share the current flow in the inductors and switching devices, so that lower current rating devices can be employed. Further, parallel connection of converters has many desirable properties, such as reduced device stresses, fault tolerance for the system, flexibility in the system design, etc. A simple two-cell interleaved boost converter is capable of extracting MP from the SCA and exhibits improved performance as compared to conventional boost converter. However, there is still a need to improve the performance (both steady state and dynamic) of the two-cell interleaved boost converter. Steady-state performance can be improved by using large inductances, which reduces the current ripple /03$ IEEE

2 750 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 4, AUGUST 2003 and conduction losses both in the switching device and filter capacitance. However, use of larger inductances slows down the transient response. To alleviate the problem of the contradictory inductance requirement for steady-state and dynamic performance, a coupled inductance approach has been reported for buck converters [11]. By introducing coupling among the parallel branches it is possible to obtain different equivalent inductances, which makes for the possibility of improving both steady-state and dynamic performance simultaneously, whereas if we use a simple two-cell interleaved converter without any coupling, one has to sacrifice the steady-state performance to achieve a faster dynamic performance or vice versa. Therefore, a coupled-inductor interleaved boost converter (CIBC) is proposed for PV applications in this paper. Use of coupled inductors [12] reduces the number of magnetic cores, exhibits lower switch stress, lower conduction losses both in the switching device and filter capacitor on account of smaller current ripple, and improves the converter efficiency as compared to the noncoupled case. In the feedforward-array-voltage-based tracking scheme, the MP point tracking depends on the adjustment of reference voltage for the feedforward loop that corresponds to the optimal array voltage at that solar insolation. If the solar insolation changes, then the optimal array voltage also changes. Therefore, an online estimation of the optimal array voltage is required for the MP point tracking control. To cope with this situation an offline artificial neural network (ANN) is proposed in this paper to estimate the optimal array voltage variation with solar insolation. For controlling the dc dc converters several control strategies are reported in the literature. These controllers are simple to implement and easy to design. However, there are several drawbacks [13] that hinder the conventional controllers, such as performance being dependent on the working point, the necessity for tuning of control parameters against changes in supply voltage and load parameters, complex design of control parameters and stabilization problems, etc. To overcome some of the disadvantages mentioned above, fuzzy logic controllers (FLCs) are appearing in industrial processes [14] [16] owing to their heuristic nature associated with simplicity and effectiveness for both linear and nonlinear systems. The main difference from conventional methods is that the accurate description of the system to be controlled is not required. Fuzzy logic allows the determination of the rule base by linguistic terms and, therefore, the tuning of the controller in a very simple way which is qualitatively different from conventional design techniques. Furthermore, fuzzy control is nonlinear and adaptive in nature, which gives it robust performance under parameter variation, load and supply voltage disturbances. In this paper, fuzzy logic has been applied to track the MP from the interleaved-boost-converter-supplied PV system. The control inputs to the fuzzy logic controller are voltage error and change of errors, while the output is the change of control signal for the pulsewidth-modulation (PWM) generator. II. MATHEMATICAL MODEL OF THE SYSTEM The basic configuration of the proposed fuzzy-neural-network-based MP tracking controller is shown in Fig. 1. Both the Fig. 1. Experimental setup of the PV system. controller and the data acquisition system are set up by using a PC, A/D and D/A conversion interfaces, and the other peripherals such as voltage sensing and scaling devices, etc. The analysis of the system is carried out under the following assumptions. 1) Switching elements (MOSFET and diode) of the converter are assumed to be ideal. 2) The equivalent series resistance of the capacitance and stray capacitances are neglected. 3) The two parallel converter cells operate in the continuous inductor current mode. 4) The switches (, ) operate in an interleaved fashion. Mathematical models for the individual components are developed in the following sections. A. PV Generator Model The PV generator is formed by the combination of many PV cells connected in series and parallel fashion to provide desired value of output voltage and current. This PV generator exhibits a nonlinear insolation-dependent characteristic, mathematically expressed for the SCA [4] consisting of cells in series and cells in parallel as where, is the electric charge; is the completion factor; is the Boltzmann s constant; is the absolute temperature; is the cell series resistance; is the photo (1)

3 VEERACHARY et al.: MPPT OF CIBC-SUPPLIED PV SYSTEM USING FUZZY CONTROLLER 751 current; is the cell reverse saturation current; and and are the SCA current and voltage, respectively. For given values of SCA parameters, the characteristic depends on the solar insolation and the MP point varies with the solar insolation. Rewrite (1) as in,. With these assumptions the simplified matrices and are (2) where. Expanding the term into Taylor series and neglecting higher order terms results in the following equation [9]. where. Equation (3) defines the simplified characteristic, which is used in the simulation studies. B. CIBC Model The intermediate CIBC produces a chopped output dc voltage and controls the average dc voltage applied to the load. Further, the converter continuously matches the output characteristic of the PV generator to the input characteristic of the load so that MP is extracted from the SCA. The mathematical model of this converter operating in continuous current mode is derived using state-space averaging technique [17]. In the general case for this converter system more topologies are possible depending on the control signals, switching frequency, and load value. The analysis presented here is only for an interleaved operation with. However, the state-space model for can easily be obtained on similar lines. The conducting devices in one cycle time period for are Mode-1: (, ); Mode-2: (, ); Mode-3: (, ); Mode-4: (, ). The state-space equations for these four different modes of operation, respectively, are (see Appendix for detailed equations) Taking the average of the above four state models results in the following average state-space model: where,, and. The corresponding and matrices are (3) (4) (5) The steady-state behavior can be obtained from the following expression: From the above equations the steady-state voltage gain of the converter is If is (6) (7) (8) then the relationship between load and input voltage III. PROPORTIONAL PLUS INTEGRAL (PI) CONTROLLER The duty ratio control law is chosen as For MP tracking the above control law becomes (9) (10) (11) Integrating the above equation, the resulting duty ratio in the form of PI control law is (12) In real time the duty ratio for the converter is generated by comparing a triangular carrier signal with control (or) reference signal, i.e., the duty ratio of the converter is (13) For a given triangular carrier signal the duty ratio is proportional to the control signal. Therefore, the control law (12) becomes (14) The above control law is implemented in real time using the following discrete form: where,. Assuming identical branches (, ) results (15)

4 752 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 4, AUGUST 2003 TABLE I RULE BASE TABLE FOR THE FUZZY LOGIC CONTROLLER Fig. 2. Membership functions for E, 1E, and 1U. IV. FUZZY CONTROLLER In recent years, FLCs have been widely used for industrial processes owing to their heuristic nature associated with simplicity and effectiveness for both linear and nonlinear systems. Advantages of fuzzy logic controllers over the conventional controllers are: 1) they do not need accurate mathematical model; 2) they can work with imprecise inputs; 3) they can handle nonlinearity; 4) they are more robust than conventional nonlinear controllers. This section will briefly describe the techniques used in fuzzy logic controller, viz., fuzzification, fuzzy knowledge base, and defuzzification. In the fuzzification process the numerical variable is converted into a linguistic variable. The following five fuzzy levels are chosen for the controlling inputs of the fuzzy controller (error:, change of error: ) in the fuzzification: NB negative big; NS negative small; ZE zero; PS positive small; PB positive big. Membership functions for controller inputs, i.e.,, and incremental change in the controller output are defined on the common normalized range of [ 1,1]. In this paper, asymmetric triangular membership functions are considered and their representation is shown in Fig. 2. These membership functions are more dense at the center and, thus, provide more sensitivity against variation in the SCA terminal voltage. This occurs particularly in cases where the change in the terminal voltage tends to zero. A. Fuzzy Knowledge Base The rule base that associates the fuzzy output to the fuzzy inputs is derived by understanding the system behavior. In this paper, the fuzzy rules are designed to incorporate the following considerations keeping in view the overall tracking performance. 1) When the SCA terminal voltage is much greater than the MP point voltage, then change the duty ratio of the converter so as to bring the terminal voltage to. 2) When the SCA terminal voltage is less than the MP point voltage, then the change of duty ratio is negative and it must be large so as to bring the terminal voltage to. 3) When the array voltage is close to the, then the incremental duty ratio is small. 4) When the array voltage is near to MP point voltage and is approaching it rapidly, then the change of duty ratio should be zero so as to prevent operating point deviation away from the MP point. 5) When the array voltage is equal to the MP point voltage, then the change of duty ratio should be maintained at zero. Taking the above points into consideration the fuzzy rules are derived and the corresponding rule base is given in Table I. These rules can be employed in any PV system for MP point tracking irrespective of size and type of converter used. There are several possible combinations of the degree of supports with varying strengths to the corresponding rules, to satisfy different conditions. However, for the present problem one such combination for the degree of support, resulting in better tracking performance, is 1.00, 0.5, 0.3, 0.0. B. Defuzzification In the defuzzification process the crisp value of the change of duty cycle is obtained. This paper uses the well-known center of gravity [17] method for defuzzification. The crisp value of control output is computed by the following equation: (16) where is the maximum number of effective rules, is the weighting factor, and is the value corresponding to the membership function of. Using the steps mentioned above, the fuzzy controller is implemented in real time for MP point tracking. The data acquisition system samples the terminal and reference voltages of the feedforward loop and computes error, change of error signals at each sampling time. Employing these error and change of error signals, the fuzzy controller determines the control action required from the fuzzy knowledge base. Then it computes the required change in the control voltage for the PWM generator, which changes the duty ratio of the converter so as to bring the terminal voltage to the desired value. In the

5 VEERACHARY et al.: MPPT OF CIBC-SUPPLIED PV SYSTEM USING FUZZY CONTROLLER 753 The learning stage of the network is performed by updating the weights and biases using the backpropagation algorithm with the gradient-descent method in order to minimize a mean-squared-error performance index given as The synaptic weights updating expressions are (21) (22) (23) Fig. 3. Feedforward neural network. where and are learning and momentum factors, respectively. With the above equations in the forward direction, the ANN training is performed. following section the method of estimating the reference voltage for the feedforward loop using neural networks is discussed. V. ANN ANNs are widely accepted as a technology offering an alternative way to solve complex problems. Particularly, in recent years the application of ANN models in various fields is increasing because these ANNs operate like a black box model, requiring no detailed information about the system. They learn the relationship between the input and output variables by studying the previously recorded data. These trained ANNs can be used to approximate an arbitrary input output mapping of the system. Among the available training algorithms, the backpropagation algorithm [18] is one of the most widely used, because it is stable, robust, and easy to implement. We now begin by considering the feedforward neural network consisting of a single hidden layer with sigmoid activation function. An input vector is applied to the input layer of the network as shown in Fig. 3. The net input of the hidden unit is (17) where is the weight on the connection from the th input unit, for represents the bias for hidden layer neurons. Now, the output of the neurons in the hidden layer is written as and the net input to the neurons in the output layer becomes (18) (19) Finally, the output of the neurons (reference voltage for the feedforward loop ) in the output layer is (20) VI. EXPERIMENTAL SYSTEM DESCRIPTION The basic configuration of the proposed PV system is shown in Fig. 1. The data acquisition system is set up by using PC, an Interface AZI-3503 card, which mainly consists of 8-channel 12-bit A/D, D/A converters. For power measurements a digital power meter (YOKOGAWA-WT130) is used, through which a general purpose interface bus (GPIB) interface is connected to the PC to record the SCA power data. The PWM modulator is a voltage comparator made of an LF311 operational amplifier. The reference signal to this comparator is the signal obtained from the D/A converter, generated by means of the MPPT algorithm. A synthesized YOKOGAWA function generator (FG120) was used to obtain phase displaced triangular carrier signals to the PWM generator. The experimental prototype circuit was built with an International Rectifier IRF530 N MOSFET with suitable driver circuit, and the diode FML-32S. The artificial sun is realized in the laboratory by means of an incandescent lamp set. Further, the solar insolation level illuminated on the solar panel is adjusted by controlling the power to this incandescent lamp set. The coupled inductor is made of a toroidal core (TDK: HF70 T) whose dimensions are mm. To avoid saturation an air gap of about 1 mm is introduced in the core. The measured values of this coupled inductor are: mh, mh, and mh. To make fair comparison and to improve the performance of the converter, the inductance values both for coupled and noncoupled cases are designed [11] satisfying the relation, where is the coupling coefficient, and and are the self inductances of noncoupled and coupled cases, respectively. VII. EXPERIMENTAL RESULTS AND DISCUSSIONS Comprehensive simulation studies were made to investigate the feedforward-voltage-based MP point tracking capability of CIBC-supplied PV system. To verify the theoretical analysis and modeling equations developed in the previous sections, a design example was considered. The SCA and converter parameters are given in Tables II and III, respectively. The PV array is simulated using (3), whereas the converter and duty ratio control are defined by (7), (15), and (16). These sets of equations are programmed in the MATLAB environment. The simulated peak power tracking characteristics for one solar insolation

6 754 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 4, AUGUST 2003 TABLE II PV ARRAY PARAMETERS TABLE III CONVERTER PARAMETERS Fig. 4. Experimental V 0 I =V 0 P characteristics of SCA module for different solar insolations. are shown in Figs To validate the simulation results, experimental investigations are presented in the following paragraphs. In order to confirm the analysis and to evaluate the performance of the proposed fuzzy feedforward-voltage-based MP point tracking, a prototype experimental PV-supplied converter system was constructed. The experimental, characteristics of the PV generator for different solar insolations are shown in Fig. 4. The comprehensive experimental studies were made to investigate the influence of coupled inductor interleaved boost converter as an intermediate MP point tracker for the PV-supplied system. Based on the theory given in the preceding sections, a program was developed in the C environment for real-time MP tracking of the SCA. The solar insolation equivalent voltage signal was measured with the help of an insolation meter, and the SCA input voltage was measured by a sensing and scaling device. These two voltage signals were sensed by the A/D converter of the data acquisition system. The offline ANN sets the reference voltage to the feedforward loop from the known solar insolation equivalent voltage signal. At a given solar insolation for MP operation of the SCA, the array operating voltage must be made equal to the MP point voltage by changing the converter duty ratio. The duty ratio is controlled by means of a PWM generator control signal, obtained from the fuzzy controller. This fuzzy controller works based on the controlling inputs, error, and change of error signals, generated with the help of the feedforward loop. The error signal generated by the feedforward loop depends on the instantaneous array voltage and the reference voltage. The reference voltage for the feedforward loop is to be set such that it is equal to the MP point voltage at that solar insolation. Since the solar insolation is varying, the corresponding reference voltage for the feedforward loop should also change according to the insolation variation. Therefore, for MP point tracking control, an online determination of insolation-dependent reference voltage for the feedforward loop is essential. Since, the optimum array voltage is nonlinearly related to the solar insolation, the linear function approximation techniques were not suitable. Under these circumstances, the ANNs provide a viable solution for the online estimation of the insolation-dependent reference voltage. In these studies a three-layer feedforward neural network with sigmoid activation function is considered for the online estimation of reference voltage. Experiments were conducted on the chosen PV generator to determine the MP point voltages. At different solar insolations the SCA terminal voltage, corresponding to MP point operation, was experimentally determined. For illustration its variation with solar insolation is shown in Fig. 5. Taking these values as reference patterns, the feedforward ANN is trained. The gradient-descent algorithm is used in training, as it improves the performance of the ANN, reducing the total error by changing the weights along its gradient. The learning rate parameter is 0.5 and a momentum factor of 0.9 is used for satisfactory performance. This trained ANN is tested for different solar insolations other than reference patterns. The test results are also plotted in Fig. 5. The results obtained from the offline-trained ANN are in close agreement with the experimental results. Now, this trained ANN is combined with the peak power tracking scheme for online estimation of the reference voltage to the feedforward loop at different solar insolations. The reference voltage variation with solar insolation, estimated from the offline-trained ANN, is also shown in Fig. 5.

7 VEERACHARY et al.: MPPT OF CIBC-SUPPLIED PV SYSTEM USING FUZZY CONTROLLER 755 Fig. 5. Feedforward loop reference voltage variation with solar insolation. Fig. 7. Error in the terminal voltage tracking at solar insolation 9. Fig. 6. Experimental SCA terminal voltage tracking characteristics at solar insolation 9. Fig Experimental SCA power tracking characteristics at solar insolation The developed scheme tracks the MP point continuously by adjusting the SCA terminal voltage to MP point voltage. At a solar insolation the experimental tracking characteristics, viz., terminal voltage, error in the terminal voltage, and SCA power are shown in Figs. 6 8, respectively. In Fig. 8, simulation results are slightly higher than the experimental values. This is mainly due to the factors that the simulated characteristic was obtained assuming ideal conditions, whereas the experimental observations include nonidealities of the converter, changes in the parameter values of the PV generator, errors involved in measuring systems, etc. Studies were also made to observe the effectiveness of the developed tracking scheme for changing solar insolations. Experimental observations show that the developed algorithm is capable of tracking MP point even for variable solar insolations. The experimental array power tracking characteristics for different solar insolations (,,, and ) are also obtained as shown in Fig. 9. For verification, MP points were determined experimentally by connecting a variable load resistance. Comparing the tracking characteristics (Fig. 9) with the MP points, it can be noticed that the feedforward control strategy is capable of extracting MP from the SCA. The tracking capability of the converter system is also verified under partial shading conditions. For illustration, array power tracking characteristic, when four cells shaded by 50% is shown in Fig. 10. Under this condition, the SCA power output decreases and settles to a new MP point as shown in Fig. 10. The proposed fuzzy-controller-based scheme is evaluated by comparing its tracking performance with that of a conventional PI-controller-based scheme. Among the possible set of controller parameters, we have chosen the optimum parameters for the PI controller (based on the operator experience, trial and error experiments) that results in better tracking performance. For illustration, the experimental MP tracking characteristics at solar insolation with the conventional PI controller are plotted in Figs It can be noticed from these

8 756 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 4, AUGUST 2003 Fig. 9. Experimental SCA power tracking characteristics for different solar insolations. Fig. 11. Experimental SCA power tracking characteristics for battery load. Fig. 10. Experimental SCA power tracking characteristics for partial shading condition. characteristics that the fuzzy controller improves the tracking performance over the conventional PI-controller-based PV system. In particular, for variable solar insolations the fuzzy controller provides better performance compared to the PI controller. Under variable solar insolation conditions, if the conventional PI controller is used for achieving the optimum tracking performance, it requires tuning of the controller gains appropriately depending on the solar insolation. From the experimental results, it is observed that the feedforward control strategy with fuzzy controller is a promising one with reference to peak power tracking. Furthermore, it does not require any tuning of the parameters, which is the case with the conventional PI controller, wherein the controller gain parameters need to be changed when solar insolation changes. The validity of the proposed tracking scheme is also verified for a battery-charging application. For illustration, experimental results were presented for one particular solar insolation Fig. 12. Experimental SCA power tracking characteristics with different membership functions. in Fig. 11. From these observations it is understood that, in this case the fuzzy control also improves the power tracking performance as compared to the PI-controller-based power tracking. The tracking performance of the converter with fuzzy controller also depends on the type of membership functions considered. In these studies, triangular membership functions as shown in Fig. 2 for,, are used. Experimental tracking performance is obtained with: 1) triangular membership functions concentrated at the center (Membership-1:, ) and 2) equal base and 50% overlap with other neighboring membership functions (Membership-2:, ). These power tracking characteristics are plotted in Fig. 12. From these results, it is observed that the triangular membership functions (Membership-1:, ), result in faster tracking performance as compared to equal base and 50% overlap with other neighboring membership functions. Faster tracking response may be due to the

9 VEERACHARY et al.: MPPT OF CIBC-SUPPLIED PV SYSTEM USING FUZZY CONTROLLER 757 TABLE IV COMPARISON OF EXPERIMENTAL RIPPLE AMPLITUDES AND EFFICIENCY FOR NONCOUPLED AND COUPLED CONVERTER SYSTEM shape of membership functions. The more dense at the center of membership functions the more will be the sensitivity. However, experimental results show slight overshoot with dense membership functions. This may be due to the degree of support given for the fuzzy rules and errors involved in the data acquisition system. A comparative study of noncoupled and coupled interleavedboost-converte-supplied PV systems is made and their steadystate performance parameters were measured. For illustration, experimental ripple amplitudes of array current, load voltage, switch current, and efficiencies at different loads are tabulated in Table IV for comparison purposes. Tabulated measurements show that the inverse coupling among the inductors will have a substantial effect on reducing the ripple content, especially load voltage and switch current stresses. The improvement in the efficiency is about 2% 5%. The observed reduction in load voltage ripple is about 50% 70%, while in the switch current stress is 70% 80%. This point clearly indicates that lower current rating switching devices are sufficient for the coupled converter system. The above experimental results agree well with those obtained from computer simulations. However, slight differences in these results are attributed to the following factors: 1) the analysis was made on the assumption that all the switching devices are ideal, i.e., nonidealities of the converter parameters such as forward voltage drops, on-state resistances of the switching devices, and other parasitics are not taken into account; 2) use of simplified PV array model; and 3) errors in the measuring system, drops in the parasitics, etc. Analytical expressions for the PV source and converters were derived. An offline ANN, trained using the backpropagation algorithm, was utilized for online estimation of reference voltage in the feedforward loop. Simulation and experimental results demonstrated the peak power tracking capability of the proposed scheme. It was also demonstrated that the fuzzy control improves the tracking performance compared to the conventional PI controller and, thus, avoids the tuning of controller parameters. Furthermore, use of coupling among the inductor branches improves the steady-state performance without deteriorating the dynamic performance. APPENDIX VIII. CONCLUSIONS A fuzzy feedforward-voltage-based MP point tracking scheme was developed for the CIBC-supplied PV system.

10 758 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 4, AUGUST 2003 REFERENCES [1] B. K. Bose and P. Szczsny, A microcomputer based control of residential photovoltaic power conditioning system, IEEE Trans. Ind. Applicat., vol. IA-21, pp , Sept [2] C. Hua, J. Lin, and C. Shen, Implementation of a DSP controlled photovoltaic system with peak power tracking, IEEE Trans. Ind. Electron., vol. 45, pp , Feb [3] C.-Y. Won, D.-H. Kim, S.-C. Kim, W.-S. Kim, and H.-S.Hack-Sung Kim, A new maximum power point tracker of photovoltaic arrays using fuzzy controller, in Proc. IEEE PESC 94, 1994, pp [4] Z. Salameh and D. Taylor, Step-up maximum power point tracker for photovoltaic arrays, Sol. Energy, vol. 44, pp , [5] A. S. Kislovski, Power tracking methods in photovoltaic applications, in Proc. Power Conversion Conf., June 1993, pp [6] T. Hiyama, S. Kouzuma, T. Imakubo, and T. H. 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Cuk, A general unified approach to modeling switching converter power stages, in IEEE PESC 76, vol. 4, 1976, pp [18] B. Widrow and M. A. Lehar, 30 years of adaptive neural networks: perceptran, madaline, and backpropagation, Proc. IEEE, vol. 78, pp , Sept Mummadi Veerachary (M 99) was born in Survail, India, in He received the Bachelor degree from the College of Engineering, Anantapur, JNT University, Hyderabad, India, in 1992, the Master of Technology degree from the Regional Engineering College, Warangal, India, in 1994, and the Dr.Eng. degree from the University of the Ryukyus, Nakagami, Okinawa, Japan, in From 1994 to 1999, he was an Assistant Professor in the Department of Electrical Engineering, JNTU College of Engineering, Anatapur, India. From October 1999 to March 2002, he was a Research Scholar in the Department of Electrical and Electronics Engineering, University of the Ryukyus. Since July 2002, he has been with the Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India, where he is currently an Assistant Professor. His fields of interest are power electronics and applications, modeling and simulation of large power electronic systems, design of power supplies for spacecraft systems, control theory application to power electronic systems, and fuzzy neuro-controller applications to photovoltaic systems. Dr. Veerachary was the recipient of the IEEE Industrial Electronics Society Student Travel Grant Award for the year 2001, Best Paper Award at the International Conference on Electrical Engineering (ICEE-2000) held in Kitakyushu, Japan, and Best Researcher Award for the year 2002 from the President of the University of the Ryukyus. He is a Member of the IEEE Industrial Electronics Society, Institute of Electrical Engineers of Japan, and Institution of Engineers India. Tomonobu Senjyu (A 89 M 02) was born in Saga Prefecture, Japan, in He received the B.S. and M.S. degrees from the University of the Ryukyus, Nakagami, Okinawa, Japan, in 1986 and 1988, respectively, and the Ph.D. degree from Nagoya University, Nagoya, Japan, in 1994, all in electrical engineering. Since 1988, he has been with the Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, where he is currently a Professor. His research interests are in the areas of stability of ac machines, advanced control of electrical machines, and power electronics. Dr. Senjyu is a Member of the Institute of Electrical Engineers of Japan. Katsumi Uezato was born in Okinawa Prefecture, Japan, in He received the B.S. degree from the University of the Ryukyus, Nakagami, Okinawa, Japan, in 1963, the M.S. degree from Kagoshima University, Kagoshima, Japan, in 1972, and the Ph.D. degree from Nagoya University, Nagoya, Japan, in 1983, all in electrical engineering. Since 1972, he has been with the Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, where he is currently a Professor. He is engaged in research on stability and control of synchronous machines. Dr. Uezato is a Member of the Institute of Electrical Engineers of Japan.