| 摘要: |
| 为了提高电能质量扰动分类准确率，针对扰动信号时序性的特点，采用了基于卷积-长短期记忆网络的电能质量扰动分类方法。首先，将扰动信号进行采样作为输入。然后，通过卷积神经网络(CNN)提取特征数据，再对提取到的特征数据以序列的形式作为长短期记忆网络(LSTM)的输入，对特征数据进行筛选更新。最后，再对输出的特征数据进行学习分类。仿真结果显示，该方法对电能质量扰动信号的平均分类准确率为99.6%，优于单一的CNN法和单一的LSTM法。 |
| 关键词: 电能质量 扰动分类 深度学习 卷积神经网络 长短期记忆网络 |
| DOI：10.19783/j.cnki.pspc.190288 |
| 投稿时间：2019-03-18修订日期：2019-05-09 |
| 基金项目:国家自然科学基金项目资助(61503240，61603246) |
|
| Classification for power quality disturbances based on CNN-LSTM network |
| CAO Mengzhou,ZHANG Yan |
| (Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China) |
| Abstract: |
| In order to improve the power quality disturbance classification accuracy, aiming at the characteristics of timing, a power quality disturbance classification method based on convolution-long short-term memory is used. Firstly, the disturbance signal is sampled as input. Secondly, the feature data is extracted via convolutional neural network. Then the extracted feature data is used as input of long and short term memory network in the form of sequence, and the feature data is screened and updated. Finally, the output feature data is classified by learning. The simulation results show that the average classification accuracy of this method for power quality disturbance signals is 99.6%, which is better than the single CNN method and the single LSTM method. This work is supported by National Natural Science Foundation of China (No.61503240 and No.61603246). |
| Key words: power quality disturbance classification deep learning convolutional neural network long short-term memory |
