摘要
针对传统组合开关不能对潜在故障进行预测的问题,提出一种基于人工智能的故障诊断方法,该方法通过实时采集组合开关内流过高压断路器的电流与高压断路器触点的温度,对比正常电流下的温度与故障状态下的温度值,采用BP神经网络,诊断断路器故障状态。通过MATLAB软件对所提方法进行仿真验证,结果表明,该方法不仅能够准确识别断路器的缺相故障,也可以准确判断潜在的故障点,从而证明了该方法的可行性和有效性。
With the improvement of coal mine information intelligence,combined switch has been widely used in fully mechanized face,aiming at the problem that traditional combined switch can not predict the potential failure,presents a fault diagnosis method based on artificial intelligence,which collects the current flowing through the circuit breaker and the contact temperature of the circuit breaker in real time,by comparing the temperature under normal current with that under fault state,the BP neural network is used to diagnose the fault state of circuit breaker.The simulation results of the proposed method by MATLAB software show that the proposed method can not only accurately identify the phase-missing fault of circuit breakers,but also accurately identify the potential fault points,the feasibility and effectiveness of this method are proved.
作者
常国祥
刘岫岭
王鸿伟
CHANG Guoxiang;LIU Xiuling;WANG Hongwei(Huzhou Vocational and Technical College,Huzhou 313000,China;Heilongjiang University of Science and Technology,Harbin 150022,China;Kailuan Energy and Chemical Co.,Ltd.,Tangshan 063000,China)
出处
《煤炭技术》
CAS
2024年第6期265-268,共4页
Coal Technology
基金
湖州职业技术学院人才基金(2022GY15)
2023年度湖州市揭榜挂帅立项项目(2023JB01)。
关键词
BP神经网络
组合开关
故障检测
断路器
BP neural network
combined switch
fault and judgment
circuit breaker
