Power Grid Fault Diagnosis Based on Improved Deep Belief Network. (July 2020) - Zhang, Yihui; Zhang, Yuansheng; Wen, Libin; Cui, Zhimei; He, Yini; Liu, Guangshi
Abstract: It is of great significance to quickly and accurately identify power system faults. This paper introduces the idea of deep learning into power system fault diagnosis research and proposes a fault diagnosis model based on improved deep confidence network. Construct a set of 30-dimensional original features that can reflect the fault characteristics of the power system as the model input, and the fault diagnosis result is the model output. Use multi-layer Boltzmann machines to train the mapping relationship between grid faults and system features. Finally, the extreme learning machine is used to supervise the labeled samples to modify the network parameters. Different system failure scenarios were set up on the New England 10-machine 39-node system to test the diagnosis ability of the model. Simulation results show that the improved deep belief network has a strong feature extraction capability. The improved deep belief network has higher accuracy and faster speed in fault categories, fault areas, and fault locations than common artificial intelligence methods.