Article Preview
Top1. Introduction
Photovoltaic systems are a superior technology for generating electricity for electric utility applications, in particular for autonomous applications. The main advancement is to improve the operation of the PV system by utilizing new techniques to extract the maximum power available from the PV array (Gosumbonggot & Fujita, 2019a). The idea of the maximum power point tracker (MPPT) is to track the maximum power available in the PV systems by controlling its terminal voltage. The power voltage characteristic curve of a uniformly distributed irradiance PV array has only one peak which can be tracked easily using conventional MPPT techniques, such as incremental conductance (IC), perturb and observe (P&O), hill climbing constant voltage techniques, etc. (Dhimish, 2019; Kihal et al., 2018; Loukriz et al., 2019; Pati & Sahoo, 2019; Ramli & Salam, 2019).
The PV array under partial shading condition (PSC) occurs when the PV modules connected in series and parallel receive different radiations due to varied reasons such as trees, clouds, dust or buildings. Partial Shading Conditions decrease the generated power extremely as the shaded modules where the P-V curve will have a unique global peak and multiple local peaks (Abdel-rahman et al., 2018; Ahmad et al., 2019; Bahrami et al., 2018; El-Helw et al., 2017; Gosumbonggot & Fujita, 2019b; Hosseini et al., 2019; Krishna & Moger, 2019; Necaibia et al., 2019).
The conventional MPPT techniques cannot track the global peak, and due to this reason, these techniques will not be researched anymore in this field. Meta-heuristic optimization techniques are able to track the global peak in case of Partial Shading Conditions. In MPPT many meta-heuristic optimization techniques have been used, such as Genetic algorithm (GA) (Alshafeey & Csaba, 2019; Khan et al., 2018; Venkateswari & Sreejith, 2019), Particle Swarm Optimization (PSO) (Alshareef et al., 2019; Džakula et al., 2019; Eltamaly et al., 2019; Ibrahim, 2019; Ma et al., 2019; Naga Durga et al., 2019; Tatsuhiko Mitsuya & Alvarenga de Moura Meneses, 2019; Trivedi et al., 2019; Valladolid et al., 2019; Veerapen et al., 2019), Differential Evolution (DE) (Narayanam et al., 2019; Somashree Pathy et al., 2019; Zijing et al., 2018), Ant Colony Optimization (ACO) (Priyadarshi et al., 2019), Harmony Search Algorithm (HSA) (Aarich et al., 2016; Othman, 2017), Artificial Fish Swarm Algorithm (AFSA) (Mao et al., 2016), Artificial Bee Colony (ABC) (Narayanam et al., 2019), Shuffled Frog Leaping Algorithm (SFLA) (Kaveh et al., 2019), The Cat Optimization Algorithm (COA) (Belhachat & Larbes, 2019), Moth Flame Optimization (MFO) (Belhachat & Larbes, 2019), Firefly Algorithm (FA) (Kasdirin, 2017; Panda et al., 2018), Flower Pollination Algorithm (FPA) (Subha & Himavathi, 2017) and Bacteria Foraging Optimization Algorithm (BFOA) (Sharma & Kumar, 2018).