DETECTION OF PARTIAL SHADING AND SHORT-CIRCUIT FAULTS IN A PV SYSTEM USING THE ARTIFICIAL NEURAL NETWORK MULTILAYER PERCEPTRON TECHNIQUE

Abstract

Photovoltaic (PV) systems play a crucial role in renewable energy production but face challenges due to environmental factors such as partial shading and short circuits, which reduce efficiency and reliability. Continuous monitoring and early fault detection are essential to mitigate these issues. In this work, we propose a fault detection method that focuses on identifying partial shading and short-circuit faults using an artificial neural network (ANN), specifically a Multi-Layer Perceptron (MLP). The MLP model is trained to classify these faults under various environmental scenarios, including situations where both faults occur simultaneously.

The methodology involves simulating different fault scenarios using MATLAB/Simulink and training the MLP on input parameters such as voltage, current, and power output from the PV system. Results indicate that the proposed approach achieves high fault detection accuracy, minimizing classification errors and ensuring reliable identification of system failures.

This research highlights the advantages of integrating ANN-based fault detection methods into PV system monitoring frameworks, enabling real-time diagnostics and proactive maintenance. By improving fault detection accuracy, the proposed method contributes to the operational efficiency, safety, and longevity of PV installations. This approach can be extended to large-scale PV systems, addressing the growing demand for reliable renewable energy solutions globally.

The findings demonstrate the potential of ANN-based methods to enhance fault diagnostics, providing a scalable solution for modern PV systems and supporting the global transition to sustainable energy.