Artificial Neural Network-based Fault Detection and Classification for Photovoltaic System

Electrical Engineering

This work focuses on detecting faults in Photovoltaic (PV) systems using Artificial Neural Networks (ANNs). A fault is defined as a deviation from the standard condition, and early detection is crucial to prevent damage and ensure safety. The PV panels, enduring harsh conditions, are prone to faults that can impair system operation and longevity. ANNs, mirroring human brain behavior, offer a powerful pattern recognition and problem-solving tool. Comprising input, hidden, and output layers, ANNs process data effectively. The project's aim is to enhance efficiency by identifying fault conditions affecting power output in utility-scale PV arrays. Customized algorithms tailored for monitoring device data analysis are being developed. A framework for using feedforward neural networks for fault detection and identification is in progress. This approach promises to uphold the reliability and safety of PV systems, addressing the increasing demands for dependable technical plants. The research aligns with the rapid growth of neural computing, showcasing its potential in critical applications like fault detection.

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