Solar maximum power point tracking based on improved incremental conductance algorithm

As the global demand for clean energy continues to grow, photovoltaic (PV) power generation has become a sustainable and environmentally friendly energy sources as it does not emit greenhouse gases or pollutants and play a key role in mitigating climate change. However, challenges such as partial shading caused by trees, buildings, or clouds can produce multiple peaks in the power-voltage (P-V) curve, complicating the continuous tracking of the global maximum power point (GMPP). Traditional maximum power point tracking (MPPT) algorithms, including incremental conductance (INC) and perturbation and observation (P&O), often get stuck in local optima under these conditions, while intelligent algorithms such as particle swarm optimization (PSO) are hampered by large oscillations and uncertain convergence. To address these limitations, this work proposes an incremental conductance-based resilient adaptive step-size MPPT (INC-RASS-MPPT) algorithm.

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