A Novel Improved Grey Wolf Algorithm Based Global Maximum Power Point Tracker Method Considering Partial Shading

Abstract

Considering photovoltaic systems' sustainability and environmental friendliness, they have been widely used due to ease of installation as their cost reduces and their efficiency is improved. Analytical maximum power point tracking methods for photovoltaic system work effectively under uniform weather conditions. However, they may fall into local maximum power points due to partial shading conditions. Although numerous meta-heuristic methods can overcome these challenges, they can still be improved regarding the convergence time to the global maximum power point. This paper suggests an improved grey wolf optimization method to track global maximum power points, enhancing the convergence process and efficiency under various weather conditions. The proposed method has been verified experimentally under dynamic and real weather conditions, consisting of uniform and non-uniform weather conditions. The method provides better dynamic tracking speed and efficiency up to 82% and 1.4% compared to the basic grey wolf optimization. According to the daily performance evaluation, the IGWO reduces the runtime by up to 76% and improves energy harvesting up to 2.3% compared to basic grey wolf optimization. The obtained results validate the superiority of the method compared under partial shading conditions in terms of tracking time and accuracy.INDEX TERMS Heuristic algorithms, improved grey wolf optimization, maximum power point tracking, partial shading, photovoltaic.

Optimal Rotor Design and Analysis of Energy-Efficient Brushless DC Motor-Driven Centrifugal Monoset Pump for Agriculture Applications

“…The GWO algorithm mimics the nature of grey wolf leadership and hunting behavior [58,59]. It utilizes four types of grey wolves: alpha (α), beta (β), delta (δ), and omega (ω), to simulate hierarchical leadership.…”