Research on electrical life Trace prediction of contact based on SG-BP algorithm

Abstract

In order to prevent electrical fire in low-voltage distribution system, it is a very effective measure to predict the electrical life of various low-voltage electrical appliances running on the line. At present, it is difficult to predict the residual electrical life of AC contactors. How to establish a reasonable and reliable prediction model for the residual electrical life of AC contactors is the key to the electrical life test and characteristic research of AC contactors. In this paper, Savitzky-Golay convolution smoothing algorithm is combined with BP neural network algorithm, and the remaining electrical life prediction model of contact is established by taking the arcing time, arcing energy and contact opening phase angle as the input parameters of the prediction model. The test results show that the average relative error of BP residual electrical life prediction model is less than 4%, and the maximum relative error is less than 10%. Combined with Savitzky-Golay convolution smoothing algorithm, SG-BP residual electrical life prediction model has average relative error less than 3% and maximum relative error less than 8%, which improves the prediction accuracy of residual electrical life, and lays a good foundation for making safe and reliable judgment by making better use of electrical life trace data..