An Enhanced Fault Detection and Power Tracking in Photovoltaic Systems using Particle Swarm Optimization

The growing adoption of solar photovoltaic (PV) systems highlights their potential as a clean and sustainable energy source. However, their efficiency and reliability can be significantly impacted by environmental factors and internal faults. This research focuses on challenges like partial shading, open-circuit faults (OCF), and short-circuit faults (SCF), and presents an innovative solution that integrates the Perturb and Observe (P&O) MPPT method with Particle Swarm Optimization (PSO) to enhance fault detection and optimize the performance of solar photovoltaic (PV) systems. Short circuit faults, on the other hand, occur when there is an unintended connection between electrical nodes, potentially causing damage to the system. Partial shading conditions (PSC) can greatly diminish the efficiency of solar PV systems by causing voltage imbalances between connected panels. By integrating fault detection algorithms with the MPPT process, the proposed system can detect and reduce faults in real-time, ensuring optimal performance and reliability of the solar PV system. The PSO algorithm optimizes the parameters of the P&O algorithm to maximize power extraction from the PV panels, even under PSC conditions. Additionally, the fault detection mechanism alerts the system to any abnormalities, allowing for prompt maintenance and repair. The implemented proposed approach is validated through experimental studies under various operating conditions. Results demonstrate improved efficiency and reliability compared to traditional MPPT techniques, particularly in scenarios with PSC and fault conditions.