基于全相位谱和深度学习的串联故障电弧识别方法

Series fault arc recognition method based on an all-phase spectrum and deep learning

为准确识别低压配电网中的串联故障电弧,提出了一种基于全相位谱和深度学习的串联故障电弧识别方法。首先,从理论上推导负载畸变信号的全相位频谱特征产生机理,利用全相位离散傅里叶变换提取线性、非线性负载的全相位频谱特征量。其次,构建了基于Logistic回归的深度学习神经网络模型,并对不同负载、不同运行状态下的全相位频谱特征量进行深度学习训练。最后,对搭建的故障电弧试验平台上采样数据进行分析,结果能准确识别低压配电网是否发生串联故障电弧和甄别出故障负载的类型。试验结果验证了所提方法的有效性,并随着深度学习理论在电力系统智能化中的应用,该方法可做进一步的深入研究和推广。

To recognize the series arc fault in low voltage distribution network accurately,this paper proposes a method of series fault arc recognition based on an all-phase spectrum and deep learning.First,it deduces the generation mechanism of all-phase spectrum characteristics of load distortion signal in theory,and extracts the all-phase spectrum feature of a linear and non-linear load by an all-phase discrete Fourier transform.Secondly,it constructs a deep learning neural network model based on Logistic regression,and carries out deep learning training of all-phase spectrum characteristics under different loads and operating conditions.Finally,the sampling data of the fault arc experiment platform is analyzed.The results can accurately recognize whether a series fault arc occurs in a low voltage distribution network and distinguish the type of fault load.Experimental results verify the effectiveness of the proposed method.With the application of deep learning theory in the intelligent power system,the method is expected to be further studied and popularized.