Method for Monitoring and Identifying PV (Photovoltaic) System Failures Using Machine Learning

Abstract

Artificial intelligence techniques have been utilized to address intricate practical challenges in various domains and are gaining popularity in the contemporary era. The principal aim of this article is to assess the prediction of power generation in three distinct photovoltaic configurations and the surveillance of measurement sensors, employing artificial intelligence and data extraction, to conform to the behavior of environmental factors in the examined region. Additionally, it encompasses the incorporation of the resulting models into the SCADA system using benchmarks, allowing the operator to actively monitor the power grid. Furthermore, it provides a method for real-time simulation and anticipation of photovoltaic systems and measurement detector within the framework of intelligent system.