A Perturb and Observe Method using Dual Fuzzy Logic Control for Resistive Load

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1 A Perturb and Observe Method using Dual Fuzzy Logic Control for Resistive Load 1 SARAH ABDOURRAZIQ, 2 RACHID EL BACHTIRI 1,2 LESSI Lab FSDM, REEPER Group, EST Sidi Mohammed Ben Abdellah University MOROCCO-FEZ Sarah.abdourraziq@usmba.ac.ma, rachid.elbachtiri@usmba.ac.ma Abstract: - The output power of photovoltaic panels is highly dynamic and non-linear in nature, because it keeps changing with solar irradiation and temperature. In order to extract maximum power point from photovoltaic cells, and improve the overall system efficiency, maximum power point tracking (MPPT) techniques are required. In this paper we present a new adaptive variable step size P&O MPPT algorithm using dual- mode fuzzy control method. In this technique we combine a far tuning fuzzy control and near tuning fuzzy control. The proposed method has been evaluated by simulation using MATLAB/Simulink and compared with the variable step size P&O MPPT technique using traditional fuzzy logic control under changing irradiation condition. The results of simulation show that the proposed algorithm significantly improves the efficiency during the tracking phase as compared to a conventional P&O method. Key-Words: - MPPT, Dual fuzzy logic control, P&O, Resistive load 1 Introduction This Photovoltaic system is one kind of renewable energy source with a broad prospect and has received more and more attention within the academia and industry at home and abroad [1-2]. The photovoltaic use the solar irradiance to convert this energy (sunlight) into electricity through solar cell [3]. The solar panel has an optimal operating point which can supply the maximum power to the load under changing irradiation and temperature condition. The maximum power point (MPP) is a particular operating point. The power and voltage characteristic of PV module is extremely nonlinear and influenced by the solar irradiance and cell temperature [4]. With the nonlinearity of PV module, maximum power point tracking (MPPT) should be developed for the PV system. In order to improve efficiency, it is necessary to apply excellent MPPT technique to track the MPP of photovoltaic cell more stably and accurately. There are many techniques of maximum power point tracking (MPPT) have been proposed, the incremental conductance method (IncCond) [3]- [4], fraction of the short- circuit current [5] fraction circuit voltage open [6]. Neural network [7], fuzzy logic control and other MPPT methods [8]- [9]. In practice, the P & O method [10]-[11]-[12] is the technique most commonly used due to its low cost, ease of implementation and relatively good tracking performance, compared to other techniques. Nevertheless, the method P & O cannot follow the MPP when weather conditions change rapidly. In this paper, variable-step perturb and observe algorithm using fuzzy logic control has been proposed. Variable step-size of perturbation ensures time efficient tracking and at the same time gives better stability in output power at maximum power point. In order to determine smooth and quick variation in step-size, fuzzy logic control has been used in this paper. The simulation study was performed, and the corresponding result confirms that the proposed method can effectively improve the system performance. 2. Solar Cell Model The PV generator is essentially a P-N junction semiconductor that converts solar energy ISBN:

2 directly into electricity. The equivalent circuit is shown in Fig. 1 [1]. Fig 1: Equivalent circuit of PV cell The following equation describes current-voltage relationship of single PV cell [3]: I = Iph - I 0 (exp (Vj*q/(nK 0 *T))-1) - (V+ Rs I)/Rp (1) Where V is the PV output voltage, I is the PV output current, Iph is the photocurrent, I 0 is the saturation current, Rs is the series resistance, Rp is the shunt resistance, and q is the electronic charge, n is the diode factor, K is the Boltzmann s constant, T is the junction temperature. The Fig. 2 shows the typical output characteristics of PV cell: maximum power (Pmpp), open circuit voltage (Voc), short circuit current (Isc), which are simuled under standard conditions (insolation (S) = 1000W/m2, temperature (T) = 25 C) 3. The Perturb and Observe Method The MPPT algorithm most commonly used is the (P & O). However, it has some disadvantages such as oscillations around the MPP and slow speed response. The tracker operates periodically by comparing the actual value of the power with the previous value to determine the change (incrementing or decrementing) on the solar array voltage or current (depending on the control strategy). If the voltage of the PV generator is perturbed in one direction and dp / dv> 0, the algorithm P & O could then continue to disrupt the PV voltage in the same direction. If dp / dv <0, then we have an overrun of the MPP, the P & O algorithm reverses the direction of the disturbance. The flowchart of the traditional algorithm P & O is shown in Fig. 3. Start Read Calcul P(n) P(n)-P(n-1)=0 P(n)-P(n-1)<0 V(n)-V(n-1)<0 V(n)-V(n-1)<0 Vref= Vref -ΔV Vref= Vref -ΔV Vref= Vref+ΔV Vref= Vref +ΔV Update P(n-1) Fig 2. Output characteristics curves with different irradiation (a) U-P curves (b) U-I Return Fig 4. Traditional Perturb & Observe (P&O) Method The tracker operates periodically by comparing the actual value of the power with the previous ISBN:

3 value to determine the search direction (incrementing or decrementing) on the solar array voltage or current curve. If the voltage of the PV generator is perturbed in one direction and dp / dv> 0, the algorithm P & O could then continue to disrupt the PV voltage in the same direction. If (dp / dv) <0, then we have an overrun of the MPP, the P & O algorithm reverses the direction of the disturbance. 4. The proposed method The traditional P&O algorithm can t satisfy both performance requirements of fast dynamic response and good accuracy during the steady state at the same time. This is because, if the step-size is set to be big enough for a fast dynamic response, the oscillation around the maximum-power operating point will increase during the steady state leading to lost power generation. The new method is devoted to obtain an effective way to ameliorate the traits of both dynamics and stable state performance. The fuzzy logic dual control can track the MPP of photovoltaic arrays accurately and rapidly, when the operating point is far away from the MPP, the controller choose the Far tuning fuzzy control, the operating point track rapidly the MPP. When the operating point is near from the MPP, the controller choose the near tuning mode control, the output power from the PV array is stable and more accurate. In other way, the two FLC intervene in different time domain figure 4. The principle of this is as follows: The output voltage and output current from the PV arrays will be detected first, and then the output power can be calculated. When the N ΔP/ΔI < K1 is fulfill, the operating point is far away from the MPP, so the Far tuning mode control should be adopted, the input of dual fuzzy control ΔP and ΔI is switched in position 1. The Far tuning mode control delivered big step to track quickly MPP. If the N ΔP/ΔI < K1 doesn't satisfy, the operating point is far near from the MPP, so the near tuning mode control should be adopted, the input of dual fuzzy control ΔP and ΔI is switched in position 2. The near tuning mode control delivered small step to track more accurately MPP. DFLC systems are shown in figure 5. It consists, Fuzzification converts the variable with real value to the variable with fuzzy value, Inference engine consists of if then rules in fuzzy rule based, Defuzzification converts the fuzzy set to the real value. The input variables of the DFLC are (ΔP) and (ΔI) the variation in PV power and the variation of PV current, respectively; moreover the output of the DFLC is the variable step-size (ΔD) of the P&O algorithm. The membership function of the input and the output variables are shown in figures 6. P I Rules Fuzzification Inference Defuzzification Fig 5. The stages of the FLC. BN SN ZZ SP BP Fig 7. Membership functions of the 1 st input variable (ΔPPV). The fuzzy based rules of the FLC are illustrated in table I, which determine for each change of state of both input output one output. The output is based on the logic that if the operating point is far away from MPP, then the FLC delivered big step to track promptly MPP. After this the step decreased and approaches to zero. However, the output of the FLC defuzzified using centre of gravity method to calculate ΔD. D The dual mode control is categorized into three different sections. The principal elements of the ISBN:

4 TABLE I. FUZZY RULES BASE ΔI ΔP NB NS ZZ PS PB NB NB NS NS ZZ ZZ NS NS ZZ ZZ ZZ PS ZZ ZZ ZZ ZZ PS PS PS ZZ PS PS PS PB PB PS PS PB PB PB The output power performance of the proposed method and P&O method when the irradiation changes are illustrated in Fig.8. In each case, The temperature is set as 25 C, the irradiation is suddenly changed from 500 to 1000W/m2 at 0.3 s and from 1000 to 800W/m2 at 0.6 s. The output power and duty cycle performance of the proposed method and P&O method when temperature changes are illustrated in Fig. 9. In each case, the irradiance is set as 1000 W/m², the temperature is suddenly changed from 30 C to 20 C at 0.3 s and from 20 C to 25 C at 0.6 s. 5. Simulation Results 1000W/m² In order to verify the feasibility of the proposed method algorithm, the simulation models of the PV system are carried out in the platform of MATLAB/Simulink. A PV system which consists of MPPT controller, PWM generator and boost converter is shown in Fig W/m² (a) 800W/m² SOLAR PANEL V C1 MPPT D C2 Fig 7. Model of coupling PV generator with MPPT command R 500W/m² 1000W/m² (b) 800W/m² A SES96M PV module is adopted as PV power source, and the specifications are listed in Table II and the parameters of boost interface are shown in Table III. Fig 8. Output power of the PV for variable insolation, (a) P&O traditional method (b)p&o proposed method. 30 C 20 C 25 C TABLE II. ELECTRICAL PARAMETERS OF SES96M Maximum power(pmpp) Voltage at MPP(Vmpp) Current at MPP(Impp) Open circuit voltage(voc) 240 W 48.5 V 4.95 A 58.2 V Short circuit current(isc) 1.55 TABLE III. PARAMETERS OF BOOST CONVERTER C1 2mF C2 L R 800uF 10mH 100Ω (a) 30 C 20 C 25 C (b) ISBN:

5 Duty cycle Duty cycle Fig 9. Output power and duty cycle of the PV panel for variable temperature, (a) P&O traditional method (b)p&o proposed method. Fig. 9 (a) shows the P&O method has slow response speed and great oscillations under the temperature changes. Nevertheless, Fig. 9(b) shows that the proposed method is rapid and it has good steady state performance under the temperature changes. As demonstrates in Fig. 9 and Fig 10, the proposed method has a better dynamic and steady state performance of the PV system simultaneously. The response performance of the proposed method and P&O method with fixed step size are presented in Table VI. TABLE IV. NUMERICAL RESULTS OF THE TRADITIONAL P&O METHOD AND THE PROPOSED METHOD Algorith ms Traditio nal method Propose d method 30 C 20 C 25 C Irradian ce 800 W/m² 800 W/m² (a) 30 C 20 C 25 C (b) Temperat ure Respon se time Ripp le 20 C 0.11s ( ) V Pow er 20 C 0.14s ( )V Conclusion In this paper, a modified variable step size P&O MPPT algorithm has been presented, which is able to improve the dynamic and steady state performance of the PV system simultaneously.the proposed variable step size method solves the drawbacks in traditional P&O MPPT. The simulation results verify the feasibility and effectiveness of the proposed method. References [1] ZHANG Chao, HE Xiangning, Maximum Power Point Tracking by Using Asymmetric Fuzzy Control Combined With PID for Photovoltaic Energy Generation System, TRANSACTIONS OF CHINA ELECTROTECHNICAL, vol. 20, NO. 10, pp , Oct [2] LIU Liqun, WANG Zhixin, Variable-Voltage MPPT Algorithm and Simulation of Photovoltaic System, Journal of South China University of Technology (Natural Science Edition), vol. 37, NO. 2, pp , Feb [3] Noppadol Khaehintung, Phaophak Sirisuk, and Anatawat Kunakorn, Grid-connected photovoltaic system with maximum power point tracking using self-organizing fuzzy logic controller, IEEE Power Electronics [4] S.Lalouni, D. Rekioua, T. Rekioua, and E. Matagne, Fuzzy logic control of stand-alone photovoltaic system with battery storage, Journal of Power Sources, Volume 193, Issue2, 5 September 2009, pp [5] Hohm DP, Ropp ME. Comparative study of maximum power point tracking algorithms. Progress in Photovoltaics: Research and Applications; November p [6] Hussein KH, Muta I, Hoshino T, Osakada M. Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions. In: IEE proceedings generation, transmission and distribution. vol. 142; January, p [7] Mohammed A. Elgendy, Bashar Zahawi, "Assessment of the Incremental Conductance Maximum Power Point Tracking Algorithm", IEEE Transactions on sustainable energy, Vol. 4, No. 1, January [8] Emad M. Ahmed, Masahito Shoyama Stability study Of Variable Step Size Incremental Conductance/Impedance MPPT for PV systems, 8th International Conference on Power Electronics - ECCE Asia May 30-June 3, [9] Hiyama, T., S. Kouzuma, and T. Imakubo, Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control, Energy ISBN:

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