Integrated Perturb and Observe Algorithm to Extract Maximum Power in Hybrid Renewable Energy System

Nowadays, the development of solar photovoltaic (SPV) technologies is shifting from prototype models and pilot demonstrations to comprehensive marketable products. However, poor power conversion efficiency of the solar panel is the key challenge in the solar industry since the panels only convert a small proportion of the accessible solar energy into electrical power. Hence, an effective maximum power point tracking (MPPT) system is inevitable to increase the power conversion performance of SPV cells. Recently, researchers have developed several MPPT approaches to overcome the key challenges in SPV-based renewable energy generation. The traditional power point tracking approaches such as incremental conductance and perturb & observe (P&O) methods easily trap into the local maximum power point. Also, these approaches fail to analyze the dynamic variations in radiance conditions and fetch large oscillation amplitude and poor convergence speed. In this context, this study proposes a Tuna Swarm Optimizer-based P&O approach (TSOPO) to decrease the oscillation amplitude, enhance the convergence rate, and increase the performance of the P&O technique over diverse partial shading (PS) patterns. This method can achieve the global maximum power point for various PS patterns. The performance of the TSOPO approach is evaluated by relating its maximum power, efficiency, and tracking speed with other advanced P&O variants using metaheuristic algorithms under four different PS patterns. The results prove that the TSOPO approach provides a better convergence rate, higher tracking efficiency, and extracts maximum power from SPV panels.