Implementing ML in the detection of solar power plants anomalies using a hybrid support vector machine with grey wolf optimization algorithm

There has been a significant increase in the use of solar energy in industry, which has led to greater awareness about renewable energy from power plants and smart grids. One challenge in this field is detecting photovoltaic (PV) system anomalies. This research aims to address this challenge by using various machine learning algorithms and regression models to identify abnormalities in PV components. The goal is to determine which models can most accurately distinguish between normal and abnormal behavior of PV systems. Our findings will provide clear guidance for making informed decisions about which machine-learning approaches are most effective in this complex problem.