Classification of building electrical system faults based on Probabilistic Neural Networks

In modern buildings, increasing the probability of an electrical fault. Therefore, in the electrical fault detection, the application of fault neural network have being more and more widely. This paper presents a diagnostic method based on using Probabilistic Neural Networks (PNN) to classify forecasting. The key is training and calculating data to classify fault types, which is collected from ten different position in fault circuit. Diagnostic results show that The diagnosis results show that the PNN algorithm can have a very good diagnostic effect. The correct diagnosis rate is 90%.