An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using
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ORIGINAL ARTICLE
An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using Crow Search Algorithm (CSA) Yehya Houam1,2 · Amel Terki1 · Noureddine Bouarroudj2 Received: 8 July 2020 / Revised: 8 September 2020 / Accepted: 16 October 2020 © The Korean Institute of Electrical Engineers 2020
Abstract The field of research in maximum power point tracking (MPPT) methods is experiencing great progress with a wide range of techniques being suggested, ranging from simple but ineffective methods to more effective but complex ones. Therefore, it is very important to propose a strategy that is both simple and effective in controlling the global maximum power point (GMPP) for a photovoltaic (PV) system under changing weather conditions, especially in partial shading cases (PSCs). This paper proposes a new design of an MPPT controller based on a metaheuristic optimization technique called Crow Search Algorithm (CSA) to attenuate the undesirable effects of partial shading on the tracking performances of standalone PV systems. CSA is a nature-inspired method based on the intelligent skills of the crow in the search process of hidden food places. CSA technique combines efficiency and simplicity using only two tuning parameters. The stability analysis of the proposed system is performed using a Lyapunov function. The simulation results for three different partial shading cases that are zero, weak and severe shading confirm the superior performance of CSA compared to PSO and P&O techniques in term of easy implementation, high efficiency and low power loss, decreasing considerably the convergence time by an average of 38.53%. Keywords Global maximum power point tracking (GMPPT) · Metaheuristic · Crow search algorithm (CSA) · Partial shading case (PSC) · Photovoltaic (PV) Abbreviations GMPP Global maximum power point MPP Maximum power point MPPT Maximum power point tracking CSA Crow search algorithm PSO Particle swarm optimization PSC Partial shading case P&O Perturb and observe FPA Flower pollination algorithm ELPSO-P&O Enhanced leader PSO-P&O FLC Fuzzy logic control PV Photovoltaic
* Yehya Houam [email protected] 1
Electrical Engineering Department, Laboratory LGEB, University of Biskra, Biskra, Algeria
Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement Des Energies Renouvelables, CDER, 47133 Ghardaïa, Algeria
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List of symbols q The charge of the electron (C) T Absolute temperature (°K) k Boltzmann constant (J/K) v Particle velocity (speed) V Lyapunov function D Duty cycle ΔD Step size of the duty cycle Dkibest Best current duty cycle at iteration k Dgbest Best global duty cycle Pki PV power at iteration k for the crow i c1, c2 Acceleration coefficients r Random number w Inertial weight xik Position of crow i at iteration k mkj Memory of crow j at iteration k N Number of crows in the flock fl Is the flight length AP Awareness probability k Iteration Pkmax Maximum PV power at iteration k
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