The power–voltage curve of photovoltaic (PV) power generation system under partial shading conditions (PSC) is non‐monotonic with multiple power peaks and traditional methods do not guarantee global convergence which leads to energy loss. This paper proposes a new tracking scheme for global maximum power point (GMPP) on a shaded PV system based on plant reproduction algorithm (PRA). The concept of PRA is suitably tailored towards maximum power point tracking (MPPT) and lucidly explained in this work. The algorithm is applied towards MPPT in several shaded PV architectures through computer simulations and later verified through experimentation. Computed and measured results show that PRA quickly identifies GMPP and further provides significantly improved MPPT characteristic curves compared to established alternatives.

This paper presents an improved Genetic algorithm (GA) for Maximum Power Point Tracking (MPPT) in shaded Photovoltaic (PV) power generation systems. The proposed GA uses shrinking population wherein fitter chromosomes are retained for next generations while lesser-performing chromosomes are removed from the population sequentially. This methodology reduces convergence time while retains major advantages of GA. The method is explained lucidly and then computer simulations and experimental results on a prototype fabricated in the laboratory are presented. The practical feasibility of the new method is then showcased by applying the new theory on a 30-kW Photovoltaic (PV) power plant established in an educational institution premise. The PV plant undergoes partial shading conditions (PSC) during morning and afternoon hours due to branches of tall trees grown around the school building. The MPPT algorithm employed in the PV plant is Perturb and Observe (P&O) which fails to track global power peak at several shading conditions leading to loss of energy. The realistic shading patterns occurring on the PV plant were recorded and the new method is shown to exhibit enhanced energy yield.

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