Temperature prediction of substation equipment based on back-propagation neural network -simulated annealing

Abstract

The substation is an important part of the smart grid. The stable operation of electrical equipment in the substation is related to the stability of the grid. According to the statistical results, among the common substation equipment failures, thermal failures have a larger proportion. Thermal faults can be monitored through the surface temperature of the equipment, and predicting the changing trend of the equipment temperature in advance is of great significance for reducing equipment faults in substations. For this reason, this paper proposes a method for predicting the temperature of substation equipment based on BP neural network-simulated annealing algorithm, using equipment parameters, environmental information parameters, etc. to predict the temperature of electrical equipment in the substation, so as to realize early warning of thermal faults in substation equipment. Through comparative analysis, it is verified that the method proposed in this paper can accurately predict the temperature of substation equipment, which provides strong guarantee and support for the safety management of substation electrical equipment.

Power cable monitoring method based on UHF‐RFID and deep learning in edge computing environment

“…Ref. [19] proposed a BP neural network optimized based on Simulated annealing algorithm for temperature prediction of substation equipment, so as to realize early warning of thermal failure of substation equipment. However, there is currently limited research on the application of temperature prediction for power cables.…”