基于WAMS和曲线相似的同调机群识别方法

Coherent generator groups identification method based on WAMS and curve similarity

提出了一种基于曲线相似的电力系统同调机群辨识新方法。首先获取WAMS实时监测得到的功角轨迹曲线作为基础数据,根据离散曲线相似性的定义,将各发电机的轨迹曲线分段,借助遗传算法确定分段轨迹之间的最优相似距离,保存了轨迹段的局部特性。然后整合各段相似距离得到各发电机之间的整体相似度并以此作为聚类指标,再利用层次聚类法实现多机系统同调机组分群。该方法简单可行,不受系统模型参数和故障类型的限制。最后对IEEE39节点系统进行分析计算,该仿真实验佐证了所提算法的有效性。

This paper proposed a new method to recognize coherent generator groups based on curve similarity in power system. Firstly, the real-time perturbed trajectory of rotor angle is used as an original data, which can be obtained from wide-area measurement system （WAMS）. According to the definition of discrete curve similarity, the power an- gle trajectory of each generator is divided into several sections. The method uses genetic algorithm to determine the best similarity distance between track segments, which can keep the local features of the track segments. Then, all segment similarity distances are integrated into overall similarity between generators. The overall similarity is taken as clustering index. Then, with the hierarchical clustering method, it can achieve clustering coherent generators in multi- machine system. This method is simple and feasible, and is not restricted by the parameters of the system model and the fault type. Finally, the simulation analysis results of IEEE39 node system show that the proposed method is effec- tive.