Detecting Faults In Photovoltaic Panels And Diagnosing Them Using Hybrid Artificial Intelligence

Solar energy has advanced to rank among the top renewable energy sources in the world today due to a number of factors, including sustainability. Solar energy is a naturally replenishing renewable energy source that does not quickly run out of supply. We can depend on the sun as a source of energy for thousands of years without damaging the biosphere. Additionally, solar panels can produce a lot of electricity even in areas with little sun exposure. Monitoring and identifying problems with self-sustaining solar panels have become increasingly crucial with the rise in their utilization. In this regard, a cutting-edge strategy (Adaptive Neuro -Fuzzy Inference System) largely reliant on smart technologies and hybrid artificial intelligence is suggested to guarantee and boost energy efficiency and improve the dependability of existing solar panels in the Algerian desert. Temperature variations and variances in solar radiation intensity cause substantial climate fluctuations in this area. This suggested method makes use of real-world data gathered from the examined system to help increase electricity output and promptly address any problems that may occur, assuring a steady supply of energy to remote places. In fact, the intelligent diagnostic method has produced remarkably accurate results, enabling the timing of the studied photovoltaic panels' flaws and the early diagnosis of any system problems. As a result, these systems are more productive and maintenance downtime is decreased.