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.

中文翻译：

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基于反向传播神经网络的断路器状态评估数据驱动方法研究

摘要 针对维护要求随设备健康状态的变化而变化，为了制定最优的维护策略，提出了一种基于数据驱动的非线性断路器运行状态在线评估方法。从不同时效性的历史数据资源中，基于贝叶斯概率提升的置信度提取特征指标。然后，改进自适应误差反向传播（BP）神经网络，通过附加动量因子、自适应学习率和改进动量，对特征指标与断路器运行条件之间的非线性相关性进行建模。最后，结合巡检测试和在线监测数据，通过输出模型对设备的全景运行工况进行客观分级。