Modified grey wolf optimization for global maximum power point tracking under partial shading conditions in photovoltaic system. (15th April 2021)

• Record Type: Journal Article
• Authors: Motamarri, Rambabu; Bhookya, Nagu; Chitti Babu, B.
• Is Part Of: International journal of circuit theory and applications. Volume 49:Number 7(2021)
• Page Start: 1884
• Page End: 1901
• Publication Date: 2021-04-15
• Subjects: maximum power point tracking -- partial shading -- particle swarm optimization -- photovoltaic system; Electric circuit analysis -- Periodicals; 621.319205
• DOI: 10.1002/cta.3018
• Languages: English
• ISSNs: 0098-9886

Abstract

Summary: In the study of photovoltaic (PV) system, power‐voltage (P‐V) curves exposed to view several peaks under partial shaded condition (PSC), which brings about muddled and most extreme maximum power point tracking (MPPT) process. Under uniform weather conditions, regular MPPT algorithms such as perturb and observe (P&O), hill climbing (HC), and incremental conductance (INC) work in an effective manner. However, these conventional methods are unable to track global peak successfully under PSC. In this context, the evolutionary algorithms such as grey wolf optimization (GWO) perform better than conventional algorithms. However, the conventional GWO is not sufficient for exploration point of view to locate global best particles; and moreover, GWO deteriorates the convergence process. To overcome these drawbacks, a modified GWO (MGWO) is proposed in this paper to track global best particle, which improves the convergence process under static condition and as well as re‐initialization of parameters under dynamic conditions. The proposed method is verified using simulations as well as using experimental results. The obtained results demonstrate superiority compared to conventional GWO and HC methods under partial shaded patterns of PV array. Abstract : This paper proposes a modified updated‐position of conventional GWO (MGWO) for global best particles to extract maximum power from the PV array during partial shading conditions. Proposed technique tracks GMPP with fewer number of iterations, leading to reduction of steady state oscillations and minimum tracking period under static condition and re‐initialization parameters under dynamic shaded conditions of PV array. The obtained experimental results demonstrate superiority compared to conventional GWO and hill climbing (HC) methods under partial shaded patterns of PV array.