基于S变换与傅里叶变换的电能质量多扰动分类识别

提出了一种基于S变换、加窗插值快速傅里叶变换(FFT)和概率神经网络(PNN)的电能质量扰动检测和分类方法.应用S变换和加窗插值FFT对电能质量多扰动信号进行时频分析,获取信号的特征量.通过训练信号集上获得的特征量,训练了一个概率神经网络用于扰动分类.训练好的网络在测试信号集上的测试结果表明,对正常电压和常见的电能质量扰动,该方法具有较高的分类准确率,在训练样本数较少、噪声影响大和多扰动信号并存时仍能取得较好的分类效果.

Power Quality Disturbance Classification Based on S Transform and Fourier Transform

A new detection and classification method of power quality disturbances based on S transform,interpolating windowed fast Fourier transform(FFT) and probabilistic neural network(PNN) was proposed.S transform and interpolating windowed FFT was first applied to perform time-frequency analysis on power quality disturbance samples,and the features can then be extracted from the results.These features are then used to train a PNN for disturbance classification.Results of applying the trained PNN on a test set wit...