ABSTRACT
Over the past few years, the usage of solar PV energy has increased significantly as a result of new policies implemented around the world to minimize the use of fossil fuels. The efficiency of the PV system is strongly reliant on external conditions, as well as numerous types of failures that can lead to serious loss of energy during the system’s operation. Furthermore, due to the lack of a monitoring system and their remote location from the city, many solar PV power plants have a long downtime. The majority of solar energy power output is reduced due to a dirth of management tools and maintenance experts with the necessary expertise. In large solar plants, it is very difficult to identify the faults and to monitor the system by the operators because of inaccurate data. So a parameter like performance ratio index (PR) is used to identify the performance of the solar PV system. This PR index value does not provide the accurate output data due to variation in weather conditions. This is updated with various methods such as temperature correction, clearness index, and variable index and SVM, and kernel method to identify the faults states and to get desired output power.
