牵引变电站直流断路器机械状态监测与故障诊断研究

直流断路器作为电力系统控制和保护的最重要的开关设备之一,其可靠运行关系着电力系统的安全稳定性。直流断路器在分合闸时的振动信号能直接反映断路器的机械状态,因此选取直流断路器的机械振动信号作为研究对象。首先研究了振动传感器选型对振动信号采集的影响并确定了传感器型号,接着研究了振动信号预处理和提取振动信号特征量的方法,最后模拟了几种常见故障并用Elman神经网络对模拟故障进行诊断。处理结果表明,用小波包分解和Elman神经网络实现了直流断路器机械状态监测和诊断。

Research on mechanical condition monitoring and fault diagnosis for DC circuit breaker in traction substation

DC circuit breaker is one of the most important switch devices for controlling and protecting power system, which is reliable operation related to the safety and stability of the power system. The vibration signal can directly reflect the mechanical state of the circuit breaker during the opening and closing. Therefore, the mechanical vibration signal of the DC circuit breaker is selected as the research object. Firstly, this paper studies the influence of vibration sensor selection on vibration signal acquisition and determines the vibration sensor. Then, it studies the vibration signal preprocessing and the method of extracting the characteristic quantity of vibration signal. Finally, several common faults are simulated and simulated faults are diagnosed by Elman neural network. The results show that the DC circuit breaker mechanical state monitoring and diagnosis is realized by wavelet packet decomposition and Elman neural network. This work is supported by National Key Research & Development Program of China (No. 2017YFB1201200).