基于改进局部异常因子算法的拓扑辨识技术

针对电网中的拓扑错误和不良遥测信息严重影响电网的安全运行的现象，提出了基于改进局部异常因子算法的拓扑辨识方法.该方法利用统计理论对开关及刀闸的状态信息和电网的遥测信息进行评估，同时考虑到遥测及遥信信息对拓扑错误辨识的影响不同，采用相对熵对其数据进行加权处理，并在异常拓扑状态检测过程中，通过网格来屏蔽那些非异常的对象，提升算法效率.实验结果表明，该算法能够快速识别电网中的拓扑错误，发现其中的不良遥测信息.

Topology Identification Based on Optimized Local Outlier Detection Algorithm

In view of the fact that grid topology errors and bad telemetry information affect the safe operation of power grids seriously, this paper proposes a topology identification method based on improved local outlier factor algorithm. The method uses statistical theory to evaluate the state information of breakers and disconnectors and telemetry information of the power network. While taking into account the different influence of telemetry and remote communication on topology error identification, we weight its data by relative entropy. In the process of abnormal topology state detection, non-anomalous objects are shielded to enhance efficiency of the algorithm. Experimental results show that the algorithm can quickly identify the grid topology errors and find bad telemetry information.