摘要
针对智能变电站条件下,因采样速率过低导致行波测距无法应用的问题,提出了一种面向智能变电站的输电线路工频综合故障定位方法。所提方法可以在现有各种单端工频测距方法中,选择出测距精度最高的方法,从而给出准确的故障位置信息。目前现有的各种工频测距方法的测距精度会受到电源、对端系统阻抗以及过渡电阻等参数的综合影响,在不同的故障条件下,各方法的测距精度会有不同的表现。首先获取输电线路发生故障时的大量训练样本,应用粗糙集理论对训练样本进行属性约简,找出测距精度与故障条件之间的内在关系。在系统发生故障时,应用KNN算法在多种工频测距方法中找到测距结果最准确的方法,计算故障距离。ATP仿真结果及RTDS仿真实验显示,所提方法可以成功避开误差较大的方法,选择实际精度最优的方法,有效提高测距精度。
In a smart substation and due to the low sampling rate, travelling wave fault location cannot be applied. Therefore, a novel algorithm for transmission line fault location in smart substations is proposed. The algorithm can select the best fault location method among the conventional single-ended frequency fault location methods, and then get a value that is the closest to the fault. The conventional frequency fault location methods have various source of error, and fault location result is affected by power source, the opposite system impedance, transition resistance and other parameters. In different fault cases, every method has different location accuracy, namely the most accurate location method always varies with varying parameters. In this paper, firstly we construct a large number of training samples of transmission line fault, secondly the rough set theory is used to reduce training samples and find the intrinsic relationship between fault location and system parameters. When transmission line has a fault, this algorithm can search for the most accurate fault location method by k-Nearest Neighbor method and finally give the most credible fault distance. The results of ATP simulation and RTDS simulation show that the algorithm can successfully avoid the method of larger system error and select the optimal method and greatly improve the fault location accuracy.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2016年第11期40-45,共6页
Power System Protection and Control
基金
国网甘肃省电力公司科技项目(522722140043)
