Research on data-driven method for circuit breaker condition assessment based on back propagation neural network. (September 2020) - Geng, Sujie
Highlights: The feature indicators are quantitatively extracted from the actual operating data, for the online and comprehensive assessment of the operating condition of circuit breakers. With the difference in timeliness, the effectiveness of the test indicators and online monitoring indicators is different in the condition assessment. The correlation between the feature indicators and the operating conditions of circuit breakers is modeled nonlinearly, in line with the complexity of the failure mechanism. Based on the feature data of the equipment during operation, the panoramic health status is quantitatively graded online, for the selection or development of the optimal maintenance strategy. 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. Graphical abstract: Image, graphical abstract