基于S变换和PSO-GRNN的柔性直流输电线路故障测距方法

Fault location method for VSC-HVDC line based on ST and PSO-GRNN

针对现有柔性直流输电线路接地故障的神经网络故障测距算法中,训练样本过多、训练时间较长、且未对鲁棒性提出有效验证的问题,提出一种基于S变换和粒子群(PSO)算法优化广义神经网络(GRNN)的线路故障测距算法。从故障行波能量谱的角度出发,采用S变换提取故障暂态电压信号能量谱,然后对表征各频率区间的能量进行求和,以实现对能量特征样本的准确提取;再将归一化处理后的能量样本输入神经网络进行训练,并采用PSO算法对GRNN的光滑因子进行优化,以提高网络收敛速度和训练精度。最后,通过电磁暂态仿真证明该方法定位精度高,不易受过渡电阻影响,在输入样本存在测量误差以及外界噪声干扰的情况下,最大误差仍低于1.5%,具有一定的工程运用价值。

Aiming at the existing neural network fault location algorithms for ground faults on VSC-HVDC lines,there are too many training samples,long training time,and no effective verification of robustness is proposed.A method based on ST and PSO optimizes the line fault location algorithm of GRNN.From the perspective of the fault traveling wave energy spectrum,the ST is used to extract the fault transient voltage signal energy spectrum,and the energy representing each frequency interval is summed to achieve accurate extraction of the energy characteristic samples;and then normalized the subsequent energy samples and input to the neural network for training,and the PSO algorithm is used to optimize the smoothing factor of the GRNN to improve the network convergence speed and training accuracy.Finally,the electromagnetic transient simulation proves that the method has high positioning accuracy and is not easily affected by the transition resistance.In the case of input samples with measurement errors and external noise interference,the maximum error is still less than 1.5%,which has certain engineering application value.