| 引用本文: | 邵宝珠,李胜辉,白雪,黄旭龙,杨晓梅.非负约束自动编码器在电缆早期故障识别中的应用[J].电力系统保护与控制,2019,47(2):16-23.[点击复制] |
| SHAO Baozhu,LI Shenghui,BAI Xue,HUANG Xulong,YANG Xiaomei.Application of nonnegative constraint autoencoder in cable incipient fault identification[J].Power System Protection and Control,2019,47(2):16-23[点击复制] |
|
| 摘要: |
| 电缆早期故障的准确识别有助于降低电力系统的故障停电率和提高供电可靠性。在传统模式识别方法中，利于分类识别的有效特征通常难以选择，从而影响识别的准确度。鉴于此，将非负约束自动编码器(Non-negative Constrain Autoencoder, NCAE)堆叠形成的深度学习(Deep learning, DL)网络应用于电缆早期故障识别中。为了提高DL网络的学习效率，首先对故障相电流进行平稳小波变换，提取出一些具有相关性、冗余性的统计量、能量熵和信息熵等作为初级特征，其次堆叠多个NCAE构建出DL网络，通过预训练和微调机制，从初级特征中获得更易于早期故障分类识别的有效特征，最后利用Softmax分类器从正常状态和其他扰动信号中识别出早期故障。利用电缆电流仿真数据进行实验，结果表明与传统模式识别方法相比，所提方法识别准确率更高。 |
| 关键词: 电缆早期故障识别 SWT变换 非负约束自动编码器 深度学习网络 |
| DOI：10.7667/PSPC201860 |
| 投稿时间：2018-07-20 |
| 基金项目:国网辽宁省公司科技项目(5602/2018-28001B)“电能质量监测数据分析与主动应用关键技术研究” |
|
| Application of nonnegative constraint autoencoder in cable incipient fault identification |
| SHAO Baozhu,LI Shenghui,BAI Xue,HUANG Xulong,YANG Xiaomei |
| (Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd, Shenyang 110006, China;College of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China) |
| Abstract: |
| Accurate identification of cable incipient fault is helpful to reduce the failure rate of power system and improve the reliability of power supply. In the traditional pattern recognition method, it is difficult to select the efficient features, which are beneficial to classification, therefore, it would affect the accuracy of recognition. In view of this, this paper applies the Deep Learning (DL) network stacked from multiple non-negative constraint autoencoders to recognize cable incipient fault. In order to improve the learning efficiency of DL network, firstly, stationary wavelet transform of the fault phase current is used to extract the primary characteristics with correlation and redundancy, e. g. some statistics, energy entropy and information entropy. And then DL network is constructed by stacking multiple nonnegative constraint autoencoders. After preliminary training and fine-tuning training process, some effective features for recognition are learned from primary features. Finally, Softmax classifier is used to identify the cable incipient fault from normal state and other disturbances. Experiments on cable current simulation data are done, and the results show that the method is more accurate than the traditional pattern recognition method. This work is supported by Science and Technology Project of State Grid Liaoning Electric Power Company (No. 5602/2018-28001B). |
| Key words: cable incipient fault identification SWT transform nonnegative constraint autoencoder deep learning network |
