基于AI+神经网络的数字化故障电弧识别方案研究

线路故障电弧的特征随着电力电子技术的提高更趋复杂，传统的故障电弧识别方式已无法应对新型用电系统。对比传统故障电弧识别方案，研究故障电弧产生类型和新型检测技术，提出基于谐波因素、总谐波畸变率、电流零休时间、电流变化速率、电流周期性等典型时域、频域特征的AI+神经网络的数字化故障电弧识别方案。该方案运用回归算法向量机对特征图进行分类处理，通过正向传输运算、反向传输运算和迭代回归运算三个步骤执行卷积神经网络，频繁迭代回归以获得最佳特征识别图。本文以调光器的故障电弧识别为例，对故障识别方案进行验证，结果表明采用此方案可实现更高效、更精准、更稳定地识别故障电弧。

Research on Digital Fault Arc Identification Scheme based on AI+ Neural Network

With the improvement of power electronic technology,the characteristics of line fault arc become more complex,and the traditional fault arc identification method has been unable to cope with the new power system.Compared with the traditional fault arc identification scheme,the generation type and new detection technology of fault arc are studied,a digital fault arc recognition scheme based on AI+neural network is proposed,which is based on typical time-domain and frequency-domain characteristics such as harmonic factor,total harmonic distortion rate,current zero rest time,current change rate,current periodicity,etc.In this scheme,the regression algorithm vector machine is used to classify the feature map,and the convolution neural network is implemented through three steps of forward transmission operation,reverse transmission operation and iterative regression operation.Frequent iterative regression is used to obtain the best feature recognition map.This paper takes the fault arc identification of dimmer as an example to verify the fault identification scheme.The results show that this scheme can achieve more efficient,more accurate and more stable fault arc identification.