GSJ: Volume 11, Issue 4, April 2023 ISSN 2320-9186 1 GSJ© 2023 www.globalscientificjournal.com GSJ: Volume 11, Issue 4, April 2023, Online: ISSN 2320-9186 www.globalscientificjournal.com REVIEW OF AN INTELLIGENT SELF DIAGNOSTIC MODEL FOR FAULT DETECTION IN PHOTOVOLTAIC SYSTEM 1 Roseline U. Paul, 2 Raphael O. Okonkwo, 3 Ekene S. Mbonu, and 4 Chukwuma D. Anyiam 1&2 Department of Computer Science, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria, 3 Department of Mechatronics, Federal university of Technology Owerri, Imo State, Nigeria 4 Department of Computer Science, Federal university of Technology Owerri, Imo State, Nigeria 1 ru.paul@unizik.edu.ng, 2 ro.okonkwo@unizik.edu.ng , 3 ekene.mbonu@futo.edu.ng and 4 chukwuma.anyiam@futo.edu.ng 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 s ystem 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 the review on an intelligent self- diagnostic model for fault detection in photovoltaic system. Key Words: intelligent self-diagnostic model, photovoltaic energy systems, solar energy, fuzzy logic system. I. INTRODUCTION Sunlight is by far the most potent energy source that Earth receives and it comes from the sun .Its use is growing day by day since it could provide for the needs of all humanity. Energy is one of the major building blocks of modern society. Energy is needed to create goods from natural resources and to provide many of the services we render or receive. The use of energy resources has relieved us of much hard works and made our efforts more productive. Energy is the power derived from the utilization of physical or chemical resources, especially to provide light and heat or to work machines (Appiah et al, 2019). Energy sources are of two kinds, Renewable and Non-Renewable energy resources. In Ram et al, (2017), a non-renewable energy resource (also called a finite resource) is a resource that does not renew itself at a sufficient rate for sustainable economic extraction in meaningful human time-frames. An example is carbon-based, organically-derived fuel. The original organic