Research on data-driven method for circuit breaker condition assessment based on back propagation neural network

Abstract

As the maintenance requirements are changed with the health status of equipment, in order to develop an optimal maintenance strategy, a data-driven nonlinear method is proposed to online assess the operating condition of circuit breakers. From the historical data resources with different timeliness, feature indicators are extracted based on the confidence improved by Bayesian probability. Then, an adaptive error back propagation (BP) neural network is improved to model the nonlinear correlation between the feature indicators and the operating conditions of the circuit breaker, by additional momentum factor, self-adaptive learning rate and improved momentum. Finally, combined with the inspection test and online monitoring data, the panoramic operating condition pf the equipment is objectively graded by the output model. Taking 500kV SF6 high-voltage circuit breaker as an example, combined with the data provided by China Yunnan Power Grid, the effectiveness of the proposed method is proved by sample tests and method comparison.