基于ACO-ENN算法的高压直流输电线路故障测距技术

Fault Location Technology for High Voltage DC Transmission Lines Based on ACO-ENN Algorithm

基于Elman动态神经网络(Elman dynamic Neural Network,ENN)计算高压直流输电线路故障测距,选择蚁群优化(Ant Colony Optimization,ACO)算法优化ENN阈值和权值,通过MATLAB与PSCAD软件展开联合仿真,研究结果显示,应用ACO-ENN能够在很大程度上提升网络收敛速度和精确度。因为ACO算法鲁棒性比较强,并实现全局最优解搜索,所以该方法有助于提高网络训练收敛速度,防止陷入局面最优。ACO-ENN算法可在直流输电线路故障测距领域应用与推广。

Based on Elman dynamic Neural Network(ENN),cable fault location of high-voltage direct current transmission line is calculated,Ant Colony Optimization(ACO)algorithm is selected to optimize the threshold and weight of ENN,and joint simulation is carried out through MATLAB and PSCAD software.The research results show that the application of ACO-ENN can greatly improve the Rate of convergence and accuracy of the network.Because ACO algorithm has strong robustness and realizes global optimal solution search,this method helps to improve the Rate of convergence of network training and prevent falling into the optimal situation.ACO-ENN algorithm can be applied and popularized in the field of Cable fault location of direct current transmission lines.