Bayesian optimized ensemble artificial neural network approach for fault detection in photovoltaic solar cell for sustainable energy

Abstract

Nowadays, a big photovoltaic (PV) farm is operating to use solar energy as a source of electricity. Finding and estimating electrical problems on these farms is crucial to ensure the system is reliable, extract the maximum energy from it, and minimize maintenance costs. Machine learning algorithms are the tools that enable the detection of faults in the panel, thereby minimizing downtime. However, changes in PV technologies or environmental conditions make model use difficult because models must be updated frequently to be accurate. This paper presents an ensemble approach of machine learning to tackle this issue. A dataset obtained from a 25 KW PV power farm is used to categorize panels in four classes, including three fault types: string fault, string-ground fault, and string–string fault, and forth one is without fault. Initially, a feature reduction technique is employed, reducing the feature size from 30 to 4. Subsequently, a Bayesian-optimized ensemble approach utilizing the bagging method is applied to identify three types of faults, as well as normal conditions. Experimental evaluation suggested that even with just 4 features, the overall classification rate is maintained at 100% accuracy on the training dataset and at 95% on the test dataset.