Condition Monitoring & Fault Detection in Photovoltaic Modules Using Machine Learning

There has been a significant increase in the use of renewable energy solutions such as Photovoltaic (PV) power plant projects to decrease the dependency on fossil fuels while still meeting the global energy demands. However, these technologies come with their own set of problems. Condition monitoring and maintenance of PV modules are one of the challenges that need to be tackled to make these new energy solutions even more efficient. The cleaning of PV modules and the maintenance of hardware components used in a power plant are significantly important to the total energy yield and efficiency of the project. This paper provides a detailed look into the application of machine learning algorithms for condition monitoring and fault detection in photovoltaic modules. This paper employs various regression models to predict the power output of the PV modules accurately. The model takes inputs from the inverter connected to an array mesh of PV modules and applies a trained prediction model that can work in real-time. The system is based on comparing linear, Bayesian and non-linear regression models on their ability to explain the variance of the yield by PV modules.