摘要： Photovoltaic is encountering a quick innovation development since a decade ago. Yet, strange conditions, for example, shortcomings, low irradiance and so forth it influence the yield of PV framework. To enhance the execution of and productivity of PV framework, it is important to create enhanced blame location procedures. This paper for the most part centres around recognition conspire for LL and LG blames in the PV cluster. Such blames stay undetected under irradiance conditions, especially, when a most extreme power point following calculation is in administration. In the event that these shortcomings are undetected, there is extensively loss of yield of PV framework, in the event that these issues are not recognized for longer time, it might harm the board and conceivably cause fire dangers. The exhibited blame identification conspire utilizes Multi-Resolution Signal Decomposition (MSD) procedure and two machine learning calculations to be specific Fuzzy Logic and K-Nearest Neighbor (KNN) to group the blame and decide its area. Reenactment results confirm the exactness, unwavering quality and versatility of the exhibited plan.