Abstract
With the continuous improvement of substation automation, the number of communication equipment has risen sharply, the network topology is more complicated, and the problem of unable to locate the fault of process-level communication networks quickly and accurately has become more and more prominent. Under this background, to improve the maintenance efficiency of process-level communication networks in the smart substation, this article proposes a method for fault location of process-level communication networks based on deep neural networks. Based on the redundant monitoring of the fault state, the fault feature information of different monitoring nodes in the message transmission process is analyzed, and the characterization method of the fault feature information is proposed accordingly. Based on the emergence principle, it realizes the automatic generation of fault samples according to the physical connection, logical connection, and message subscription relationship of process-level communication networks. Combined with the training rules in deep learning theory, a fault location model of process-level communication networks based on deep neural networks is established, and the real-time location steps are given. Taking the process-level communication network of a typical 110kV smart substation as an example, simulations verify the effectiveness and accuracy of the proposed fault location method in single fault environments and multiple fault environments. Besides, accurate results can be obtained even when the fault feature information is wrong or missing, and the anti-interference ability is excellent.
Highlights
Smart substations have the remarkable characteristics of intelligent main equipment and networked auxiliary equipment [1], [2]
With the process-level communication network of smart substations is becoming more sophisticated, aiming at the problem that the fault is challenging to be accurately located, this article proposes a method for fault location of process-level communication networks based on deep neural networks (DNN)
Combined with the training rules in deep learning theory, a fault location model of process-level communication networks based on DNN is established, and the real-time location steps are given
Summary
Smart substations have the remarkable characteristics of intelligent main equipment and networked auxiliary equipment [1], [2]. The faults in the process-level communication network mainly occur in the auxiliary equipment ports, switches, and fiber-optic links [4] Aiming at these faults, the technicians at this stage mostly judge the cause based on the functions such as message records and traffic monitoring in the Network Analyzer (NA). When a fault occurs on the process-level communication network, the subscriber of the message can only issue an alarm, but cannot locate the fault directly [5] On this basis, xi proposed parsing GOOSE and SV messages and combined with traffic control, by setting three security lines to ensure the reliable transmission of critical messages [6]. In real-time fault localization, the fault point can be obtained by taking the fault feature information as input and calculating with the DNN location model
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