FUZZY LOGIC BASED PARTICLE SWARM OPTIMIZATION MPPT TECHNIQUE FOR POWER CONDITIONING SYSTEM

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1 Volume 114 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu FUZZY LOGIC BASED PARTICLE SWARM OPTIMIZATION MPPT TECHNIQUE FOR POWER CONDITIONING SYSTEM Subrahmanya Bhat 1 and Dr.Rajkiran Ballal I 2 1 SDM Institute of Technology, Ujire INDIA muddumanya@gmail.com 2 Mangalore Marine College and Engineering, Kuppepadavu INDIA. ballalrk@yahoo.co.in May 30, 2017 Abstract This paper proposes an innovative process of performance optimization of maximum power point tracking (MPPT) using evolutionary algorithms. The proposed approach is a hybrid model of Fuzzy logic technique and particle swarm optimization (PSO) technique. According to the proposed approach, Fuzzy logic is implemented as MPPT controller and maximum power point is tracked. Further an improvement in maximum power tracking is obtained by using PSO optimization technique. The main aim of this model is to reduce the steady state oscillation and to track the maximum power during varying environmental conditions.the MAT- LAB Simulink tool is used to evaluate the performance of proposed approach. Finally, an experimental study is made on proposed approach gives the best performance compared with the other MPPT techniques. Corresponding Author 1 57

2 Key Words and Phrases: Particle swarm optimization (PSO), maximum power point tracking (MPPT), Photovoltaic (PV),Fuzzy logic controller. AMS subject classification : 34H, 94D 1 Introduction A renewable energy resource helps to mitigate the carbon content from the environment resulting in reducing the issue of global warming. The Photovoltaic (PV) power conversion is considered as the most efficient energy resource for power generation. By considering this, an attempt is made on hybrid system for energy generation in the rural areas [1]. For the residential applications, deployment cost is a challenging task for the researchers. To overcome this issue, a new technique for quick installation and reduction in the cost of deployment has been found out [2]. This technique is based on the Plug-and-Play (PnP) methodology. Similar studies have done by combining photovoltaic and wind turbines [3]. Sometimes these resources face various uncertainties which degrade the performance of any grid system. In this study authors aimed on the behavior analysis of wind and photovoltaic resources. The MPPT is used widely in PV power conversion systems. This controller helps to extract the maximum power from a photovoltaic module and improves the power conversion efficiency of the photovoltaic system. Various techniques have been discussed from past decade to address the issue of power tracking. Power tracking maximization results in improved performance of the photovoltaic systems. The perturbing and observing method [4] and the incremental conductance method [5] are the most commonly used techniques. Both the methods consider fixed step size for the increment of the duty ratio command. According to MPPT algorithms, power converters play a vital role where increment and decrement in duty ratio can maximize the output power by considering voltage versus power curve or voltage versus current curve. Most of the MPPT based techniques for renewable energy uses perturbing and observing methods. A similar technique is presented in [6]. However, MPPT techniques are widely used for real-time applications due to its multiple advantages such as low-cost implementation and reliability etc. In this work authors have addressed the issue of MPPT technique using 2 58

3 perturb and observe MPPT algorithm. In conventional studies it was reported that the techniques suffer from steady state condition which causes energy wastage. Another main issue with the conventional MPPT techniques is that the algorithms provide inaccurate performance during varying atmosphere condition. Along with the perturbing and observing technique, incremental conductance method [7] is also well-known for improving the performance of any grid connected system. Recently based on MPPT various techniques have been presented for improving the performance of PV grid systems. Open-circuit based MPPT voltage technique is presented for improving the performance [8]. This technique is discussed to address the issue of weather changing condition scenario. In order to carry out this, a real-time analysis was performed for more than six months with the help of PV test module. In modern systems microcomputer or Digital Signal Processing (DSP) models are used to determine the optimal operating point [9]. These models cannot perform in the complex circuit scenario by degrading the performance due to high cost of implementation and lower operating speed. An adaptive approach for perturbing and observing MPPT technique has been developed recently [10]. This model follows a three-fold working process where the first step is to obtain the adaptive tracking, in the second step steady-state value around the maximum power point is obtained and finally a concept is introduced which doesnt require any pre-defined system dependent constant. A new approach has been introduced known as hill-climbing method for the MPPT where maximum power point is obtained using variable step-size and optimal value is achieved using incremental conductance method [11]. This technique helps to mitigate the fixed step-size resulting in less number of iteration for processing. Similarly in [12] [13], authors have introduced incremental resistance techniques for MPPT modeling. However, these techniques still need to be improved to meet the satisfactory energy saving criteria. The current work attempts for PV array modeling Designing a fuzzy logic technique based controller for PV array Implementation of the PSO model for optimizing the performance of PV system. 3 59

4 Comparative analysis to validate the significance of proposed approach 2 Proposed Model This section deals with the proposed methodology for PV system performance improvement. First of all,the modeling of PV array and its mathematical model is made. 2.1 PV modeling : In this subsection, PV array modeling is presented. The Figure 1 shows the equivalent circuit of PV cell. This circuit contains diode, series resistor, parallel shunt resistor and photo current. Figure 1: MPPT controller for 5 kw PV-System For any ideal cell, output current can be expressed using Kirchhoffs current law. The mathematical expression of cell current is given as Eq. 1. I = I ph I d I sh (1) Where I p h denotes photo current, diode current is given by I d and parallel resistance current is denoted by I s h. ( ) ] qud I d = I O [exp 1 (2) F kt c Similarly, diode current I d can be expressed as given Eq. 2. Where electric charge q, k is Boltzmanns constant, F is the ideal factor of 4 60

5 diode, saturation current of diode is denoted by I O and voltage of equivalent diode is given by U d. 3 MPPT control strategy: The MPPT techniques mainly aim at the maximization of the output power obtained from PV array. Figure 2 shows the Power versus Voltage characteristics of any standard Photovoltaic system. In this work the fuzzy logic controller model for PV grid connected Figure 2: MPPT controller for 5 kw PV-System systems have been developed. This controller requires two input parameters as voltage and current and the controller helps to synthesize the MPPT technique by considering temperature and solar irradiation scenario. The inputs to fuzzy logic controller are taken from P-V and V-I curve. s. Based on these voltage and power values the membership functions D(t) and CD(t) calculated In order to compute the membership functions for fuzzy logic controller module, total amount of tracked voltage and power from PV system is considered. By considering power and voltage parameters, membership functions are computed as expressed in Equation. (3). D (t) = P pv (t) P pv (t 1) V P V (t) V pv (t 1) (3) CD (t) = D (t) D (t 1) Where P pv instant power generated in the photovoltaic array, D(t) denotes the error value in the system and CD(t) denotes change in 5 61

6 Table 1: Fuzzy rules for PV system (CE) NB NS ZE PS PB NB ZE ZE PB PB PB NS ZE ZE PS PS PS ZE PS ZE ZE ZE NS PS NS NS NS ZE ZE PB NB NB NB ZE ZE error value in the proposed system. After applying fuzzy logic controller scheme, we introduce PSO based optimization to obtain the optimal value for maximum power point. This process of particle swarm optimization (PSO) is discussed in next subsection. 3.1 PSO with Fuzzy Logic Controller for PV System : In this section PSO process is discussed by considering the following criteria: Improving the efficiency of algorithm: this is applied for reducing the cost of energy generation and provides better performance in terms of energy generation. Tracking direction recognition: this helps to understand the rapid fluctuation in environment conditions and respond to the changes by sensing the direction of change. Steady state error reduction According to the PSO algorithm, it adapts the search and behavior resulting in the best solution in the given search space. Table 1 presents the total rules considered for controller where fuzzy system is applied. The parameters for the simulation of the proposed system are given in Table

7 Table 2: parameters of the proposed PV system Parameter Name Experimental consideration Simulation Tool MATLAB 2016b Cell temperature 25 degree Light Generated current IL 9.44 Amp Double Diode saturation current 3.23e-10 Diode ideality factor Shunt resistance 47.9 Ohms Series resistance 0.22 Maximum power (W) Open circuit voltage 51.5 Voltage at the maximum 43 power point Temperature coefficient Cells per module 80 Short-circuit current 9.4 Current at maximum power 8.13 point 4 Experimental Results and Discussions The following figure 3 shows implementation of proposed approach for PV systems. This model of MPPT controller designed for the 5 kw PV-system also includes pulse width modulation generator module along with Fuzzy Logic controller which is optimized using PSO model. Figure 4 shows the maximum power point tracking voltage and PV voltage. Figure 5 shows proposed voltage and current performance analysis by considering fuzzy-pso implementation. It can be seen that fuzzy logic controller based PSO MPPT gives superior output. 5 Conclusion In this work, an attempt is made for obtaining the advanced MPPT model to extract the maximum power from the PV system. To achieve this, a combined model is developed where Fuzzy Logic 7 63

8 Figure 3: MPPT controller for 5 kw PV-System Figure 4: MPPT & PV voltage versus duty ratio Figure 5: voltage & PV voltage versus duty ratio with hybrid fuzzy- PSO MPPT Controller and PSO techniques are incorporated. The current work uses weight adaption model to speed up the searching of optimal particles in PSO. For the PSO, greater step sizes are considered to increase the velocity during search in local space. In this work, PV array is modeled for 5kW output. Experimental study is carried using MATLAB Simulink simulation tool. Experimental analysis 8 64

9 shows that proposed approach has a robust performance for PV systems and is capable to track the maximum power for varying environment conditions and reduces steady state errors. References [1] L. R. Almeida Gabriel Filho, O. J. Seraphim, F. d. L. Caneppele, C. P. Cremasco Gabriel and F. Ferrari Putti, Variable analysis in wind photovoltaic hybrid systems in rural energization, IEEE Latin America Transactions, 14 (2016), [2] M. T. A. Khan, G. Norris, R. Chattopadhyay, I. Husain, S. Bhattacharya, Auto-Inspection and Permitting with a PV Utility Interface (PUI) for Residential Plug-and-Play Solar Photovoltaic Unit, IEEE Transactions on Industry Applications, 19, (2016),1-1 [3] X.Ran and S.Miao, Three-phase probabilistic load flow for power system with correlated wind, photovoltaic and load, IET Generation, Transmission & Distribution, 10 (2016), [4] Femia, N., Petrone, G., Spagnuolo, G., Vitellio, M. Optimization of Perturb and Observe Maximum Power Point Tracking Method. IEEE Trans. Power Electron. 20, 2005, [5] Yong, T., Xia B., Xu Z. Sun, W. Modified Asymmetrical Variable Step Size Incremental Conductance Maximum Power Point Tracking Method for Photovoltaic Systems. J. Power Electron. 14, (2014), [6] N.Mutoh, TMatuo, K.Okada and M.Sakai, Prediction-databased maximum-power-point-tracking method for photovoltaic power generation systems, IEEE 33rd Annual IEEE Power Electronics Specialists Conference. 3, (2002), [7] Q.Mei, M. Shan, L. Liu and J. M. Guerrero, A novel improved variable step-size incremental-resistance MPPT method for PV systems, IEEE Trans. Ind. Electron., 58 No. 6,(2011)

10 [8] K. Kobayashi, H. Matsuo and Y. Sekine, A novel optimum operating point tracker of the solar cell power supply system, IEEE 35th Annual Power Electronics Specialists Conference 3 (2004), [9] A. K.Abdul salam, A. M. Massoud, S. Ahmed, and P. N. Enjeti, High-Performance adaptive perturb and observe MPPT technique for photovoltaic-based micro grids, IEEE Trans. Power Electron., 26 No. 4, (2011) [10] S. Jain and V. Agarwal, A new algorithm for rapid tracking of approximate maximum power point in photovoltaic systems, IEEE Trans. Power Electron. Lett., 2 No. 1, (2004), [11] P. E. Kakosimos and A. G. Kladas, Implementation of photovoltaic array MPPT through fixed step predictive control technique, Renewable Energy, 36,(2011) [12] W. M. Lin, C. M. Hong and C. H. Chen, Neural-Network- Based MPPT Control of a Stand-Alone Hybrid Power Generation System, IEEE Transactions on Power Electronics, 26, no. 12, (2011) [13] K. Ishaque, Z. Salam, M. Amjad and S. Mekhilef, An Improved Particle Swarm Optimization (PSO)Based MPPT for PV With Reduced Steady-State Oscillation, IEEE Transactions on Power Electronics, 27, no. 8, (2012)

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