Affiliations: [a] Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, China | [b] School of Electrical Engineering and Automation, Wuhan University, Wuhan, Hubei, China
Correspondence:
[*]
Corresponding author: Shiqi Yi, School of Electrical Engineering and Automation, Wuhan University, Wuhan, Hubei 430072, China. E-mail: nene0721ysq@163.com.
Abstract: The power metering system is an important part of the smart grid for data acquisition and analysis. The fault state of the main station directly affects the stable and safe operation of the power metering system. Hinged on the real-world data supplied by the monitoring platform of the Metrology Center of Guangdong Power Grid Co., Ltd., we present a novel malfunction diagnosis method for the main station of the power metering system. The proposed method utilizes the synthetic mi-nority over-sampling technique (SMOTE) and designs a combined model of long short-term memory (LSTM) network and ResNet. SMOTE solves the sample imbalance problem. Furthermore, the combined LSTM-ResNet model employs LSTM to extract the time-dependent signal feature and exploits ResNet to optimize data flow. Consequently, the proposed LSTM-ResNet model improves training efficiency and malfunction diagnosis accuracy. The proposed diagnosis mthod is verifird on the real-world data, which proves the proposed method’s surpass traditional methods. A specific analysis of results and the practical application of the proposed method is also elaborated.
Keywords: Malfunction diagnosis, power metering system, synthetic minority over-sampling technique, long short-term memory, ResNet
