人工神经网络方法实现自动判别失磁类型及失磁深度

The Study of Application of Artifical Neural Network on Failure Mode Recognition and Loss Degree Predition

利用失磁后 Ｅｔ．ｑ、 Ｅｆ ｑ．ｑ 衰减时间常数 Ｔｑ 仅取决于发电机参数及失磁类型的结论，根据失磁故障的分类，采用一个三层前向神经网络得出 Ｔｑ ，使微机失磁保护能够在失磁发生的瞬间自动判别出失磁类型并预测失磁深度。数字仿真、动模试验以及实际运行证明了本文所提出的方法。

Using a conclusion of time constant attenuation after loss of excitaion only decided by generator parameters and failure modes ,the paper according to the classify of loss of excitation faults,adopts a three lay feed forward neural network to obtain “T q” ,Which makes microcomputer based Loss of Excitation protection in the twinking of fault happening failure mode recognition and loss degree prediction.Figure simulating,trend imitate testing and action moving proved that the method raised is correct.