An Advanced and Robust Approach to Maximize Solar Photovoltaic Power Production

The stochastic and erratic behavior of solar photovoltaic (SPV) is a challenge, especially due to changing meteorological conditions. During a partially irradiated SPV system, the performance of traditional maximum power point tracking (MPPT) controllers is unsatisfactory because of multiple peaks in the Power-Voltage curve. This work is an attempt to understand the performance uncertainties of the SPV system under different shading conditions and its mitigation. Here, a novel hybrid metaheuristic algorithm is proposed for the effective and efficient tracking of power. The algorithm is inspired by the movement of grey wolves and the swarming action of birds, and is thus known as the hybrid grey wolf optimizer (HGWO). The study focuses on the transient and steady-state performance of the proposed controller during different conditions. A comparative analysis of the proposed technique with incremental conductance and a particle swarm optimizer for different configurations is presented. Thus, the results are presented based on power extracted, shading loss, convergence factor and efficiency. The proposed HGWO-MPPT is found to be better as it has a maximum efficiency of 94.30% and a minimum convergence factor of 0.20 when compared with other techniques under varying conditions for different topologies. Furthermore, a practical assessment of the proposed controller on a 6.3 kW(p) rooftop SPV system is also presented in the paper. Energy production is increased by 8.55% using the proposed approach to the practical system.

Automated summary

This article discusses the challenges and uncertainties in solar photovoltaic (SPV) systems, especially due to changing meteorological conditions. It introduces a novel control algorithm, inspired by the behaviors of grey wolves and birds, for effective power tracking in SPV systems. Comparative analysis shows the superiority of the proposed algorithm over other techniques. Additionally, the article presents a practical assessment of the algorithm on a rooftop SPV system, demonstrating increased energy production.