基于改进堆叠去噪自动编码器的电能质量扰动分类方法

Power Quality Disturbance Classification Method Based on Improved Stacked Denoising Autoencoders

针对当前电能质量扰动分类时因数据量大和特征量提取不足而造成分类精度低的问题,文章将压缩感知和深度学习结合,提出一种基于改进堆叠降噪自动编码器(Improved Stacked Denoising Autoencoders,ISDAE)的电能质量扰动分类方法。首先,将原始数据经过压缩感知后得到的稀疏向量作为数据集;然后构建堆叠去噪自动编码器模型,引入Inverted Dropout技术提升网络的泛化能力,避免过拟合现象的产生,并在微调阶段引入自适应矩估计(Adaptive moment estimation,Adam)优化方法,以降低陷入局部最优的概率。最后对10种常见的电能质量扰动信号进行仿真分析,可以发现所提方法降低了需要分析的数据量,解决了传统分类算法对特征选取不充分从而造成分类效率低的问题,并在一定程度上提升模型的鲁棒性。

In view of the problem of low classification accuracy which is caused by the large amount of data and insufficient feature extraction in the current power quality disturbance classification,this paper com-bines compressed sensing and deep learning to propose a power quality disturbance classification method based on improved stacked denoising autoencoders(ISDAE).First,the sparse vector obtained after the original data compressed and sensed is used as the data set.Then the stacked denoising au-toencoder model is constructed,the inverted dropout technology is introduced to improve the gener-alization ability of the network and avoid the occurrence of overfitting.In the fine-tuning stage adaptive moment estimation(Adam)optimization method is introduced to reduce the probability of falling into a local optimum.Finally,simulation analysis is performed on 10 common power quality disturbance signals.It can be found that this method effectively reduces the amount of disturbance data that needs to be processed,and solves the problem of low classification efficiency due to the insufficient feature selection of traditional classification algorithms.To a certain extent,the robustness of the model is improved.