基于S变换和弹性神经网络的电能质量扰动分类

Classification of Power Disturbances Based on S-Transform and Resilient Back Propagation Neural Network

提出了基于S变换和弹性BP神经网络结构(RPROP)的电能质量扰动自动分类方法。通过S变换对电能质量扰动信号进行时频分析,有效实现对各种扰动信号时频特征量的输出,并确定特征量的最优组合来增加弹性BP神经网络分类的精度。同时研究了在不同噪声条件下弹性BP神经网络分类的敏感度。测试结果显示,该方法能有效地对电能质量扰动信号进行分类。

An S-transform-based resilient back propagation neural network structure（RPROP） is presented for automatic classification of power quality disturbances.Firstly,through S-transform time-frequency analysis,the method detects and output kinds of PQ disturbances effectively.Then,feature components are extracted from the detecting outputs for classification.In addition,an optimum combination of the most useful features is identified for increasing the accuracy of classification.Features extracted by using the S-transform are applied as input to NN for automatic classification of the power quality（PQ） disturbances that solves a relatively complex problem.Sensitivity of proposed classifier under different noise conditions is investigated.The testing results show that the classifier can effectively classify different PQ disturbances.