| 引用本文: | 田鹏飞,于 游,董 明,等.基于CNN-SVM的高压输电线路故障识别方法[J].电力系统保护与控制,2022,50(13):119-125.[点击复制] |
| TIAN Pengfei,YU You,DONG Ming,et al.A CNN-SVM-based fault identification method for high-voltage transmission lines[J].Power System Protection and Control,2022,50(13):119-125[点击复制] |
|
| 摘要: |
| 高压输电线路故障识别对保证电网安全稳定运行具有重要意义。提出了一种基于CNN-SVM的高压输电线路故障分段识别方法。针对传统故障识别方法数据特征提取过程复杂的问题，通过深度学习的CNN模型，将故障特征以时序矩阵形式输入其卷积层与池化层，从而简化特征提取与计算过程。此外，针对高压输电线路故障特征不明显导致相间故障识别率较低的问题，提出将故障相间电流差及非故障相负序与零序分量作为特征，输入到SVM模型，进而判断相间故障接地类型。仿真结果表明，所提方法准确率高，与其他深度学习方法相比，在相间故障识别的准确率上提升尤为显著。 |
| 关键词: 故障识别 序分量特征提取 CNN SVM |
| DOI：DOI: 10.19783/j.cnki.pspc.211196 |
| 投稿时间：2021-08-30修订日期：2021-11-30 |
| 基金项目:国家自然科学基金项目资助(51477121) |
|
| A CNN-SVM-based fault identification method for high-voltage transmission lines |
| TIAN Pengfei,YU You,DONG Ming,JIANG Zhijun,BAO Pengyu,WU Guoding,ZHANG Tiandong,HU Po |
| (1. State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China; 2. State Grid Dalian Power Supply Company,
Dalian 160033, China; 3. Wuhan University, Wuhan 430072, China) |
| Abstract: |
| High-voltage transmission line fault identification is of great significance in ensuring the safe and stable operation of a power grid. This paper proposes a high-voltage transmission line fault segmentation method based on CNN-SVM. Given the complex problem of the data feature extraction process of traditional fault recognition methods, the fault features are input into convolutional and pooling layers in the form of a time series matrix through a deep learning CNN model, thereby simplifying the feature extraction and calculation process. In addition, given the problem that the fault characteristics of high-voltage transmission lines are not obvious (leading to a low recognition rate of phase-to-phase faults), it is proposed to take the current difference between the fault phases and the negative and zero sequence components of the non-fault phase as features and input them into the SVM model to determine the type of fault grounding between phases. The simulation results show that the method has a high accuracy rate. Compared with other deep learning methods, the accuracy of phase-to-phase fault recognition is improved significantly.
This work is supported by the National Natural Science Foundation of China (No. 51477121). |
| Key words: fault identification sequence component feature extraction CNN SVM |
