福州大学电气工程与自动化学院 福州 350108
本文提出一种基于GraphSAGE (Graph Sample and aggregate，GSA)算法的配电网故障定位方法。以对系统侧母线电压进行形态学黑帽运算(Morphological Black Hat)的结果启动故障定位算法；利用GSA模型自主挖掘网络拓扑和零序电流特征，根据节点特征和标签建立函数映射，评估线路运行状态从而实现故障定位。基于PSCAD/EMTDC仿真平台搭建IEEE33节点模型，测试结果表明所提配电网故障定位方法可行且有效。并且配电网拓扑变化时，该方法无需重新训练模型即能获得可靠的故障定位结果，验证了方法的鲁棒性和对拓扑变化的适应性。
A fault location method based on GraphSAGE (Graph Sample and Aggregate, GSA) algorithm is proposed in this paper. The Morphological Black Hat operation is performed on the power-side bus voltage of the distribution network as the fault detection criterion to start the fault location algorithm. The GSA model is used to independently mine topology and zero sequence current features, and function mapping is established according to node features and labels to evaluate the running state of the line to achieve fault location. Based on the PSCAD/EMTDC simulation platform, an IEEE 33-node simulation model is constructed to acquire data resources and validate the proposed method. Reliable fault location results are obtained by applying the proposed method. Furthermore, in distribution networks with topological changes, the model can obtain reliable fault localization results without retraining, which verifies the robustness and adaptability of the method to topological changes.