一种基于最大相关-最小冗余算法的输电线路故障定位方法

针对传统输电线路故障定位精度较低且易受故障后暂态分量影响的问题,提出一种基于数据驱动的输电线路故障定位方法。构建基于系统状态和故障位置的电力系统运行数据集,基于最大相关-最小冗余准则,在此数据集中挖掘关键特征与故障位置之间的隐含关系,提取与故障位置最大相关且变量之间最小冗余的关键信息,构建与故障位置最相关的核心变量集,应用曲线拟合技术获取运行变量与故障位置之间的数学解析关系,综合多个特征信息给出准确的故障定位结果。仿真验证结果表明,与传统基于阻抗的故障定位方法相比,所提方法具有更高的故障定位精度,同时对故障后出现的暂态分量也有良好的适应能力,是一种准确快速的输电线路故障定位新方法。

Transmission Line Fault Location Method Based on Maximum Correlation - Minimum Redundancy

To solve the problem of low fault location accuracy and vulnerable to transient component after fault, a data-driven fault location method for transmission lines is proposed. The method first build based on system condition and fault location of power system operation data set. Then based on the maximum correlation - the least redundancy in the centralized data mining the implied relationship between key characteristics and fault location. Extraction and fault location is the largest and related variables between minimum redundancy of key information. Build and fault location at the heart of the most relevant variables set. The last application of curve fitting technology acquisition operation variables and the mathematical relationship between the fault position. Integrated multiple characteristics information accurate fault location results are given.Simulation results show that compared with traditional impedance-based fault location method, the proposed method has higher fault location accuracy and good adaptability to transient components after fault, which is a new accurate and fast fault location method for transmission lines.