采用改进模糊C均值法的电网故障元件定位

Location of Faulty Power Grid Component Using Improved Fuzzy C-averaging Method

基于WAMS的实时测量信息,提出了利用改进模糊C均值法对广域测量信息构成的样本进行最优分类,从而定位出故障元件及故障区域的方法。仿真结果表明,该算法具有更好的全局寻优能力及寻优稳定性,在相量测量单元(PMU)测量数据含有噪声的情况下,能精确、方便、迅速地定位故障元件及故障区域,而且算法运行时间能满足广域自适应后备保护的时限要求。

With the wide application of the WAMS （wide area measurement system）, the wide area adaptive backup protection is the development trend of power system protection, but the traditional fault location methods are difficult to meet the wide area adaptive backup protection in time limits. Based on real-time measurement information from WAMS, an improved fuzzy C-averaging method is proposed in this paper. The wide area measurement information samples are optimally classified with the improved fuzzy C-averaging method. Then the fault component and fault area are located. The simulation results show that the algorithm has better global optimization ability and stability. The fault component and fault area are located accurately, conveniently and quickly on the PMU （ phasor measurement unit） measurement data containing noise. The calculation time of this algorithm is able to satisfy the time limit requirements of the wide area adaptive backup protection.