| 引用本文: | 武昭旭,杨 岸,祝龙记.基于循环神经网络的电能质量扰动识别[J].电力系统保护与控制,2020,48(18):88-94.[点击复制] |
| WU Zhaoxu,YANG An,ZHU Longji.Power quality disturbance recognition based on a recurrent neural network[J].Power System Protection and Control,2020,48(18):88-94[点击复制] |
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| 摘要: |
| 针对电能质量扰动信号识别算法复杂、识别率低等问题，提出一种将长短时记忆神经网络应用于电能质量扰动信号识别分类的新方法。首先在 Tensorflow中搭建长短时记忆神经网络，建立电能质量扰动信号分类模型。其次利用分类模型对电能质量扰动信号原始数据进行有监督学习，提取扰动信号的深层次特征，并将其连接到Softmax分类器输出各扰动信号的识别率。最后将电能质量扰动信号通过递归图生成的二维轨迹图像作为分类模型的输入，通过训练模型实现扰动信号的分类。仿真结果表明，该分类模型对电能质量扰动信号的一维和二维表示均有较好的分类准确率，可以有效识别7种单一扰动和6种复合扰动信号。 |
| 关键词: 递归图 循环神经网络 LSTM 电能质量扰动信号 分类 |
| DOI：DOI: 10.19783/j.cnki.pspc.191421 |
| 投稿时间：2019-11-13修订日期：2019-12-10 |
| 基金项目:国家自然科学基金-山西煤基低碳联合基金(U1610120) |
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| Power quality disturbance recognition based on a recurrent neural network |
| WU Zhaoxu,YANG An,ZHU Longji |
| (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, China) |
| Abstract: |
| Given the problems of a complex identification algorithm and the low recognition rate of a power quality disturbance signal, a new method of applying a long-short term memory neural network to power quality disturbance signal recognition and classification is proposed. First, a long-short term memory neural network is built in Tensorflow to establish a power quality disturbance signal classification model. Secondly, the classification model is used to supervise the original data of the power quality disturbance signal, and the deep features of the disturbance signal are extracted and connected. The Softmax classifier outputs the recognition rate of each disturbance signal. Finally, a two-dimensional trajectory image generated by the power quality disturbance signal through the recursive map is used as the input of the classification model, and the disturbance model is classified by the training model. The simulation results show that the classification model has good classification accuracy for the one-dimensional and two-dimensional representation of power quality disturbance signals, and can effectively identify seven single disturbances and six composite disturbance signals.
This work is supported by National Natural Science Foundation of China-Shanxi Coal-based Low-carbon Joint Foundation (No. U1610120). |
| Key words: recursive graph recurrent neural network LSTM power quality disturbance signal classification |
