THE BENEFITS AND CHALLENGES OF INTELLIGENT SELF-DIAGNOSTIC MODEL FOR FAULT DETECTION IN PHOTOVOLTAIC SYSTEMPDF

Abstract: The use of photovoltaic energy systems as an alternative energy source is growing in popularity. The stand-alone PV system must continue to function at its peak level in order to efficiently harness reliable energy. This requires continuous maintenance and monitoring. An intelligent self-diagnostic model for monitoring PV system is needed, nevertheless, to determine faults in the system. An intelligent self-diagnostic model is a system that performs self-diagnosis by monitoring internal signals and operations of the system for evidence of faults. The uncertainty associated with the monitoring and detection of faults in photovoltaic systems could be easily and efficiently solved using the intelligent self-diagnostic model, which are developed using artificial intelligence (AI) techniques. AI-based systems learn and train continuously in order to behave like humans and develop self-reasoning and problem-solving capabilities. Complex problems could be successfully solved by AI tools without the need for more sophisticated mathematical manipulations. As a result, AI has emerged as a promising alternative to conventional approaches to problem-solving. Artificial Neural Networks ANN, Fuzzy Logic (FL), Expert Systems (ES), Natural Language Programming (NLP), and various hybrid approaches are all examples of AI techniques, for this research work, we will base on fuzzy logic system for fault detection. This paper is on the benefits and challenges of intelligent self-diagnostic model for fault detection in photovoltaic system