Machine Learning Based MPPT Optimization for SPV Systems under Partial Shading Conditions

This paper presents various maximum power point tracking (MPPT) techniques for solar photovoltaic (SPV) systems operating under partial shading conditions (PSCs). Traditional methods such as perturb and observe (P&O) used as a base model which face significant challenges in accurately identifying the maximum power point (MPP) in the power-voltage curve. To overcome these challenges other optimization techniques like particle swarm optimization (PSO), grey wolf optimization (GWO), and cuckoo search algorithm (CSA) and machine learning (ML) is used. A multilayer perceptron (MLP) based MPPT framework designed to predict duty cycles based on SPV voltage and current inputs. Simulation results shows that the MLP-based approach achieves faster convergence in power output, and improved voltage stability compared to P&O, PSO, GWO, and CSA methods. The result highlights the potential of integrating ML techniques into SPV systems to enhance efficiency in challenging PSC scenarios. This research contributes to the advancement of sustainable solar energy technologies by leveraging adaptive intelligence for optimal energy harvesting.

کلیدواژه ها:

SPV ، MPPT ، ML ، PSO ، GWO