A novel Artificial Location Selection Optimization for Global Maximum Power Point Tracking under Partial Shading Conditions

Partial Shading Condition (PSC) is a common issue when implementing the Photovoltaic (PV) system. Conventional Maximum Power Point Tracking (MPPT) algorithms are not able to track the Global Maximum Power Point (GMPP) under PSC as the Power–Voltage (P–V) characteristic curve has multiple Local Maximum Power Points (LMPPs). In the literature, meta-heuristic algorithms have been implemented in the Global Maximum Power Point Tracking (GMPPT) applications. However, meta-heuristic algorithms often face initialization and parameter tuning issues. In this paper, a novel evolutionary-based method called Artificial Location Selection Optimization (ALSO) is proposed for GMMPT under PSC. The proposed ALSO replicates the artificial selection activity widely used in agriculture. Compared to the conventional Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), it does not involve random numbers so that the PSC issue can be solved effectively. In addition, our proposed algorithm is able to vary its tracking speed according to the complexity of the PSC. The performance of the ALSO has been tested by simulations and experiments by exploiting 252 non-repeating PSC test cases. Both simulations and experiments demonstrate that the ALSO algorithm performs better than the PSO and GA algorithms in terms of speed, efficiency, accuracy, power convergence, and duty convergence.