MPPT of a Partially Shaded Photovoltaic Module by Ant Lion Optimizer
This paper is mainly focused on the maximum power point tracking of a photovoltaic module under non uniform solar irradiation level. In such a case multiple local maximum power points are observed but only one global maximum power point exists. In this pa
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Katanov State University of Khakassia, Shetinkina, 61, 655017 Abakan, Russian Federation [email protected] Siberian State Aerospace University, Krasnoyarsky Rabochy Avenue 31, 660014 Krasnoyarsk, Russian Federation [email protected]
Abstract. This paper is mainly focused on the maximum power point tracking of a photovoltaic module under non uniform solar irradiation level. In such a case multiple local maximum power points are observed but only one global maximum power point exists. In this paper, the Ant Lion Optimizer algorithm is adopted to find the global maximum power point of a partially shaded photovoltaic module. The simulation results show that the performance of the Ant Lion Optimizer is better than the perturbation & observation algorithm in detecting the global maxima of a partially shaded photovoltaic module. Keywords: Photovoltaic module Lion Optimizer
Non-Uniform solar irradiation level Ant
1 Introduction Solar energy is the most inexhaustible and non-polluting among all the clean and renewable energy resources. The performance of a photovoltaic (PV) module is dependent on temperature, solar irradiation level, shading, and module configuration. Under partially shaded conditions, the PV module characteristics become more complex with several peaks [1]. In such situations, it is very important to extract the maximum power. But the conventional methods (such as the perturbation & observation algorithm) fail to optimally track the maximum power point (MPP) of a PV module [1]. This forms the motivation for the use of evolutionary optimization techniques such as the Ant Lion Optimizer (ALO) [2] for detection of the global MPP. The Ant Lion Optimizer is a nature inspired algorithm proposed by Seyedali Mirjalili in 2015 [2]. The Ant Lion Optimizer mimics the hunting mechanism of ant lions in nature. The benefits of the ALO algorithm are global optimization, simplicity, reliability, and effectiveness for real world tasks. The MATLAB toolbox of the ALO algorithm is publicly available at http://www.alimirjalili.com/ALO.html. The main contributions of the present work are given below.
© Springer International Publishing Switzerland 2016 Y. Tan et al. (Eds.): ICSI 2016, Part I, LNCS 9712, pp. 451–457, 2016. DOI: 10.1007/978-3-319-41000-5_45
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E.A. Engel and I.V. Kovalev
(a) Performance of the ALO algorithm in effectively locating the global MPP for a partially shaded photovoltaic module is tested and the simulation results are discussed. (b) The simulation results revealed advantages of the ALO algorithm over the conventional perturbation & observation algorithm.
2 Matlab/Simulink Model of the Photovoltaic Module Under Partially Shaded Conditions The study of the partial shading PV module is carried out based on the MATLAB/Simulink model power_PVArray_PartialShading.slx [3]. This model simulates of a 250-W PV module under partially shaded conditions. The PV module consists of 60 cells connected in series (Fig. 1). The MATLAB/Simulink model power_PVArray_PartialShading.slx contains a variable
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