Comparison of multilayer perceptron neural network architecture in photovoltaic plants fault classification

Abstract

The goal of this study is to assess the application of Multilayer Perceptron Artificial Neural Networks in fault classification within photovoltaic panels, focusing on key characteristics such as the number of neurons and layers, activation functions, training techniques, and the resulting accuracy. The study employs a comparative analysis approach, examining various characteristics and hyperparameters applied to Multilayer Perceptron Artificial Neural Networks for fault classification in photovoltaic panels. The research methodology involves reviewing publications from the past decade to gather data on these characteristics and their impact on fault analysis in photovoltaic generation systems. This study contributes to the originality of the field by providing a comprehensive comparison of various parameters and techniques used in Multilayer Perceptron Artificial Neural Networks for fault classification in photovoltaic panels. The findings offer valuable insights for researchers and practitioners in the renewable energy sector, aiding in the development of more efficient and reliable fault diagnosis systems for photovoltaic generation.