Operation Status Monitoring of Substation Equipment Based on BP Neural Network

The safe operation of substation equipment will directly affect the power supply reliability of the substation. To guarantee the stability of the substation, a monitoring approach utilizing the BP neural network for assessing the operational status of substation equipment is suggested. The Fourier transform method is used to carry out registration processing on the transformer equipment image, and the morphology and weight adaptive algorithm are combined to carry out enhancement processing on the registered image to obtain the enhanced image. Combined with the sift feature of the improved image of the substation equipment and the sparse coding of the image, the joint feature of the substation equipment is extracted and input into the designed BP neural network to monitor the operation status of the substation equipment. The experimental results show that the image quality of the substation equipment collected by the proposed method is high, the Fisher criterion function value is higher than 0.3, and the monitoring time is lower than 12.9 s, which has a good monitoring effect.