基于变模态分解的自适应遗传算法下输电网故障测距与故障辨识策略仿真

Simulation of Transmission Network Fault Location and Fault Identification Strategy Based on VMD Adaptive Genetic Algorithm

现有输电网故障测距分成基于线路参数测距和行波测距。由于线路参数受气候和运行工况影响较大,故依此原理的故障测距精度不高。行波测距方法受波速不确定性的影响,精度也不高,且EMD算法中的模态混叠现象和端点效应难以消除。本文提出基于变模态分解的自适应遗传算法,对故障电流分解本征模态函数,计算暂态高频相关系数和暂态高频能量系数用于故障类型辨识。本文设置自适应遗传算子,弥补了普通遗传算法由于交叉概率和变异概率取作常数而可能导致其收敛在局部最优的缺点,通过自适应遗传算法对模态分量进行编译、交叉、选择操作。当达到收敛条件或者测距收敛时,输出测距结果和故障辨识。最后PSCAD建立三机九节点模型仿真验证,基于变模态分解和自适应遗传算法测距准确精度在99%以上,能正确输出故障类型。

The existing transmission network fault location is divided into line parameter based fault location and traveling wave fault location. Because the line parameters are greatly affected by climate and operating conditions, the fault location accuracy based on this principle is not high. The traveling wave ranging method is affected by the uncertainty of wave velocity, the accuracy is not high, and the modal aliasing and endpoint effect in EMD algorithm are difficult to eliminate. In this paper, an adaptive genetic algorithm based on variable mode decomposition(VMD) is proposed to decompose the eigenmode function of fault current and calculate the transient high frequency correlation coefficient and transient high frequency energy coefficient for fault type identification. In this paper, an adaptive genetic operator is set to make up for the disadvantage that the common genetic algorithm may converge to the local optimum due to the constant of crossover probability and mutation probability. The modal components are compiled, crossed and selected by the adaptive genetic algorithm. When the convergence condition or ranging convergence is reached, the ranging results and fault identification are output. Finally, PSCAD establishes a three machine nine node model, which is verified by simulation. Based on variable mode decomposition and adaptive genetic algorithm, the ranging accuracy is more than 99%, and the fault type can be output correctly.