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Signal Acquisition and Data Monitoring of Electrical Equipment State
Jinyi Li and Bing Yan

The normal state of electrical equipment has an important influence on the whole power system. In this paper, a method based on temperature signal acquisition was designed for monitoring the state of electrical equipment, and the temperature signal of the equipment was collected by arranging a fiber Bragg grating sensor (FBGS) on the equipment. In order to realize the monitoring of the data, the back-propagation neural network (BPNN) algorithm was used to predict the future equipment temperature, and the grey wolf optimizer (GWO)-BPNN method was obtained by optimizing the BPNN parameters through GWO. The results showed that the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of the GWO-BPNN algorithm were 0.27, 0.78%, and 0.33 under normal conditions and 0.33, 0.84%, and 0.36 respectively under abnormal conditions, which were all better than the BPNN algorithm. The experimental results prove the reliability of the GWO-BPNN algorithm. The GWO-BPNN algorithm can be applied to actual electrical equipment to realize the state monitoring of equipment.

Keywords: electrical equipment, signal acquisition, state monitoring, equipment temperature, prediction