Fault location of secondary equipment in smart substation based on switches and deep neural networks

Abstract

With the continuous development of smart substations, the functions of switches have been continuously improved. To improve the efficiency of the operation and maintenance of secondary equipment, this article proposes a fault location method for secondary equipment in smart substations based on switches and deep neural networks. First, the key alarm signal capture function and traffic statistics function are configured on the switch. Secondly, based on alarm information, traffic statistics, and message subscription relationships, the representation of the fault feature information method is proposed. Using the training rules of deep learning, the fault location model of secondary equipment based on deep neural network is established and the fault location steps are given. Taking the line spacing of smart substations as an example, simulations verify the effectiveness of the fault location method, and good location results can still be obtained in an environment with insufficient feature information reliability.