基于LMD近似熵和SVM的自适应重合闸方法

自适应重合闸的功能是快速、准确地辨识故障性质及捕捉电弧熄灭时刻.在分析瞬时性故障和永久性故障断路器跳闸后的端电压波形复杂性的基础上,提出了局部均值分解(LMD)、近似熵和线性支持向量机(SVM)相结合的自适应重合闸整体实现方案.利用LMD分解故障信号得到若干个PF分量,选取前3个PF分量算出其近似熵值构成三维特征向量,将三维特征向量作为SVM的输入量来区分故障性质和捕捉电弧熄灭时刻.线路故障仿真结果表明,该方案可智能识别故障性质和捕捉电弧熄灭时刻且具有一定的抗噪能力.

Adaptive Reclosure Method Based on LMD-approximate Entropy and SVM

The key function of adaptive reclosing is to correctly identify fault nature and quickly capture the transient fault arc extinction time. Based on the analysis of the waveform complexity of fault terminal voltage after circuit breaker tripping under transient fault and permanent fault, this paper presented an adaptive reclosing overall implementation by combining local mean decomposition (LMD), approximate entropy and support vector machine (SVM). After getting the PF components of fault signal by using LMD decomposition, the approximate entropy of the first three PF components is calculated, which constitutes a three-dimensional feature vector as the input of SVM to identify fault nature and to capture arc extinguishing moment. Simulation results verify that this method can intelligently distinguish the transient fault from permanent fault, and capture transient fault extinction time with a strong anti-noise ability.