10kV配网电容器在线监测与故障诊断系统

On Line Monitoring and Fault Diagnosis of 10 kV Distribution Network Power Capacitor

目前大多数电力电容器状态监测系统存在实时性不足和数据频密度不足的缺点,难以实现准确的在线故障预判,易导致故障处理滞后或误报等不良后果;文章立足于电力电容器运行和维护的实际需求,构建了一套完整的电力电容器故障在线监测系统,并给出了一套采用神经网络融合电容器电流、电容、电阻和电压等信息的故障诊断模型和方法,并在实际中进行了应用。在实际应用中,该系统能及时并准确地对电容器的异常状态和故障特征进行捕捉,避免了故障判断的滞后性,提高了获得数据的准确性,能够提高电网设备的运行和维护效率,提升电网运行可靠性。

At present,most of the power capacitor condition monitoring systems are lack of real-time and data frequency density.It is difficult to achieve accurate online fault prediction,which is easy to lead to bad consequences such as delay in fault processing or false alarm.Based on the actual demand of operation and maintenance of power capacitor,this paper constructs a complete on-line fault monitoring system of power capacitor,and presents a fault diagnosis model and method using neural network to fuse the current,capacitance,resistance and voltage information of capacitor,which is applied in practice.In practical application,the system can timely and accurately capture the abnormal state and fault characteristics of capacitors,avoid the lag of fault judgment,improve the accuracy of data acquisition,improve the operation and maintenance efficiency of power grid equipment,and improve the reliability of power grid operation.