一种新的电能质量扰动识别方法

针对电能质量扰动信号数据多、识别速度慢、识别过程复杂等问题,提出一种基于压缩感知理论和一维卷积神经网络的电能质量扰动信号识别分类方法,该方法通过离散傅里叶变换、高斯矩阵获取原始扰动信号的稀疏向量,利用正交匹配追踪算法重构扰动信号,将原始扰动信号和稀疏向量输入一维卷积神经网络分类模型;由仿真结果可知,可充分降低现有识别方法所需处理的扰动信号的数据量,实现了以较少的数据量表达扰动信号的特征信息,对有、无噪声情况下的14种单一、复合扰动信号具有很高的识别率,表明了方法具有采样数据少、特征提取方便、高识别率和较好的噪声鲁棒性的特点。

A New Method for Identifying Power Quality Disturbances

Aiming at the problems of multiple power quality disturbance signals, slow recognition speed, and complicated recognition process, a power quality disturbance signal recognition and classification method based on compressed sensing theory and one-dimensional convolutional neural network is proposed. This method uses discrete Fourier transform and Gaussian matrix to obtain the sparse vector of the original disturbance signal, uses the orthogonal matching pursuit algorithm to reconstruct the disturbance signal, and inputs the original disturbance signal and the sparse vector into the one-dimensional convolutional neural network classification model. It can be seen from the simulation results that this method can fully reduce the data volume of the disturbance signal to be processed by the existing recognition method, and realize the expression of the characteristic information of the disturbance signal with a small amount of data. It has high recognition rate for 14 types of single and compound disturbance signals with and without noise, which shows that the method has the characteristics of less sampling data, convenient feature extraction, high recognition rate and better noise robustness.