Adaptive two-step power prediction and improved perturbation method for accelerated MPPT with reduced oscillations in photovoltaic systems
Qinglin Meng, Ying He, Shiguang Li, Sheharyar Hussain, Jinghang Lu, Guodong You, Josep M. Guerrero
Abstract
Improving the efficiency of photovoltaic (PV) power generation and minimizing power loss are critical challenges in PV systems. Traditional maximum power point tracking (MPPT) methods, such as the perturbation and observation (P&O) and incremental conductance (INC) methods, suffer from significant power oscillations at steady state and slow tracking speeds. This paper introduces an improved P&O algorithm that integrates a two-step power prediction with the conventional one-step approach and optimizes the duty cycle adjustment. The optimization employs an adaptive resistance coefficient that is proportional to the PV array voltage and inversely proportional to the current, enabling the algorithm to dynamically adjust its step size as it nears the maximum power point (MPP). Experimental results demonstrate that the improved P&O method reduces oscillations, improves tracking stability, and adapts effectively to varying irradiance conditions. The proposed algorithm reaches the MPP approximately 0.068 seconds faster than the traditional P&O and INC methods, achieving an average tracking efficiency improvement of 5.79 % across different environmental conditions. Additionally, under strong partial shading, the improved P&O with an optimized duty cycle achieves 9.56 % and 9.22 % higher efficiency than the traditional P&O and INC methods, respectively, while under moderate partial shading, these improvements are 2.21 % and 2.28 %, respectively. The simplicity of the improved P&O ensures ease of implementation while significantly enhancing the efficiency of PV power generation.