To locate the fault location accurately and solve the problem quickly is the key to improve the power supply capacity of power grid. This paper presents a fault location method based on SVM fault branch selection algorithm and similarity matching. Firstly, an SVM-based fault branch filter classifier was constructed based on the positive sequence component feature matrix data of each monitoring point, which can accurately select the branch where the current fault is located. Then, based on the positive sequence voltage distribution characteristics, the Euclidean distance and Pearson correlation coefficient (PCC) are used to establish the similarity objective function of fault location. And then, the fault is accurately located by the objective function. Finally, the proposed method is validated by using an IEEE-14 node network. The results show that the proposed method is effective and accurate.

中文翻译：

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基于支持向量机和相似模型匹配的故障定位方法

准确定位故障位置并快速解决问题是提高电网供电能力的关键。提出了一种基于支持向量机故障分支选择算法和相似度匹配的故障定位方法。首先，基于每个监测点的正序分量特征矩阵数据构造了基于支持向量机的故障分支滤波器分类器，可以准确地选择当前故障所在的分支。然后，基于正序电压分布特性，利用欧氏距离和皮尔逊相关系数（PCC）建立故障定位的相似目标函数。然后，通过目标函数准确地定位故障。最后，通过使用IEEE-14节点网络验证了所提出的方法。