基于小波神经网络和故障录波数据的电网故障类型识别

电力系统发生大面积复杂故障后,调度人员仅仅依靠来自数据采集与监视控制(supervisory control and data acquisition,SCADA)系统的保护和开关接点的变位信息难以做出准确的判断,来自故障录波装置记录的模拟量信息越来越成为故障诊断和系统恢复的重要依据。为了进一步提高超高压输电线路故障类型识别率和计算速度,文中利用提升小波和PNN网络构造了新的小波神经网络故障识别模型,应用bior3．1提升小波对故障电流进行分解,将分解到的 (0,375)Hz频率段的小波系数输入到PNN神经网络。通过 ATP仿真及华东电网实际故障录波数据的测试和比较结果表明:该模型具有很高的识别率和收敛速度,并有望将该模型应用到电网故障诊断系统。

A Fault Classification Method Based on Wavelet Neural Networks and Fault Record Data

When complicated faults in a large area are happened in a power system, it is difficult for management and running personnel to judge them accurately according to the shift information of relays and switches contact from SCADA system only. The analog information that comes from fault record equipments becomes the important basis of fault diagnosis and system recovery more and more. In order to improve fault recognition capability and computational speed of the fault diagnosis system, this paper presents a new wavelet neural network mode constructed from lifting wavelet and PNN neural network. The coefficients of fault currents in the low frequency band between 0 and 375 Hz that decomposed by bior3.1 lifting wavelet are put into the neural network. Through ATP simulation and the test of real fault record data from the power network in East of China, the result indicates that the mentioned model in this paper has very high recognition rate and convergence speed. It is likely to apply this model in a fault diagnosis system of a power network.