一种特高压直流输电故障测距方法研究

A Fault Location Method for UHV DC Transmission

特高压直流输电系统的输电线路一般较长,当发生故障时,准确并且快速地找到故障点并切断故障显得尤为重要,所以,研究故障测距意义重大。基于固有频率和故障距离之间存在一种非线性关系,首先通过径向基函数(RBF)神经网络对这种非线性关系进行拟合,并利用果蝇算法(FOA)对径向基函数进行优化,最后形成FOA-RBF故障测距模型并进行测距。通过大量数据进行仿真验证,结果误差均在0.1%以下,验证了该方法的准确性。

Due to the transmission lines of UHV DC transmission system are generally long, it is especially important to find the fault point and cut off the fault accurately and quickly when the fault occurs. Therefore, it is of great significance to study the fault location. Based on some nonlinear relationships between natural frequencies and fault distances, this nonlinear relationship is fitted by a radial basis function(RBF) neural network, and the radial basis function is optimized using the Drosophila algorithm(FOA) to form a FOA-RBF fault location model for ranging. Finally, a large number of data are used for simulation verification, and the error of the results is less than 0.1%, which verifies the accuracy of the method.