基于CNN-SVM的高压输电线路故障识别方法  

A CNN-SVM-based fault identification method for high-voltage transmission lines

作　　者：田鹏飞 于游 董明 姜志筠 包鹏宇 吴国鼎 张天东 胡钋[3] TIAN Pengfei;YU You;DONG Ming;JIANG Zhijun;BAO Pengyu;WU Guoding;ZHANG Tiandong;HU Po(State Grid Liaoning Electric Power Co.,Ltd.,Shenyang 110006,China;State Grid Dalian Power Supply Company,Dalian 160033,China;Wuhan University,Wuhan 430072,China)

机构地区：[1]国网辽宁省电力有限公司,辽宁沈阳110006 [2]国网大连供电公司,辽宁大连160033 [3]武汉大学,湖北武汉430072

出　　处：《电力系统保护与控制》2022年第13期119-125,共7页Power System Protection and Control

基　　金：国家自然科学基金项目资助(51477121)。

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。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.

关 键 词：故障识别 序分量特征提取 CNN SVM 

分 类 号：TM75[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]