Stacked sparse autoencoder with PCA and SVM for data-based line trip fault diagnosis in power systems

Artificial intelligence methods have been applied to power system fault diagnosis based on the switch values such as relay protection actions and electrical component actions. These methods have their own problems including availability of data, state exponential explosion, difficulty modeling, etc. This paper deals with the application of stacked sparse autoencoder (SSAE) for power system line trip fault diagnosis based on the analog quantity of operation data. SSAE, a deep learning structure, is a more effective approach to solve problems mentioned above due to its network structure and layer-wise training mechanism. It also covers the detection of incipient faults based on the strong data mining capability. In the paper, an SSAE-based network with support vector machine (SVM) and principal component analysis (PCA) is proposed to improve the accuracy of fault diagnosis in power systems. The real-world simulation experiments prove the improvement and practical application of the proposed method. The process steps and parameter selection are elaborated in detail.