Abstract: The output characteristics of photovoltaic modules are affected by many factors,so the fault detection of photovoltaic modules is a severe test.In order to ensure the real-time and accuracy of fault diagnosis,multi-sensor method is adopted to extract short-circuit and open-circuit fault features,and voltage scanning method is adopted to obtain the judgment basis of thermal breakdown and electrical breakdown caused by uneven illumination.Based on fault characteristics,an improved RBF neural network based on K-means clustering algorithm is proposed for fault diagnosis of photovoltaic module.In Matlab platform,this method is used to simulate the fault types and location of photovoltaic modules.The results show that the accuracy rate of this test method is up to 96.67%,while the accuracy rate of BP neural network is only 83.33%.It verifies the accuracy and effectiveness of the improved RBF neural network fault diagnosis method.