500 kV超高压直流输电线路雷击故障自动识别方法  

Automatic identification method for lightning strike faults in 500 kV ultra-high-voltage direct current transmission lines

作　　者：邬蓉蓉[1,2] 黄志都 徐文平 唐捷 黄维 WU Rongrong;HUANG Zhidu;XU Wenping;TANG Jie;HUANG Wei(College of Electrical and Information Engineering,Hunan University,Changsha 410082,Hunan,China;Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment,Electric Power Research Institute of Guangxi Power Grid Co.,Ltd.,Nanning 530023,Guangxi,China;Production Technology Department,Guangxi Power Grid Co.,Ltd.,Nanning 530023,Guangxi,China)

机构地区：[1]湖南大学电气与信息工程学院,湖南长沙410082 [2]广西电网有限责任公司电力科学研究院广西电力装备智能控制与运维重点实验室,广西南宁530023 [3]广西电网有限责任公司生产技术部,广西南宁530023

出　　处：《沈阳工业大学学报》2025年第2期160-167,共8页Journal of Shenyang University of Technology

基　　金：国家自然科学基金重点项目(51437003);广西电网科技项目(GXKJXM20220065)。

摘　　要：【目的】输电线路是电力系统的重要组成部分,其中大多数线路故障由雷击引起。雷击干扰识别是行波故障分析准确性的关键。为快速识别500 kV超高压直流输电线路中的雷击故障,保障电力系统的稳定运行,提出了一种基于自动化方法的雷击故障识别技术。【方法】利用字典学习算法对输电线路信号进行去噪,构建信号幅值波动最小误差目标函数,并通过热启动更新字典和牛顿迭代法优化字典矩阵,从而获得高纯度的雷击故障信号,有效降低噪声干扰,提高识别精度。采用小波时间熵技术提取去噪信号中的关键特征。通过小波变换生成小波系数,重构特定层的系数,并定义滑动时间窗以计算熵和信息量,从雷电流暂态信号中提取特征,为故障识别提供数据支持。收集不同雷击特征信号,采用集成学习算法训练特征,生成多个弱分类器,并通过权值融合为强分类器,用于分类每一个雷电流暂态信号样本,提高分类器的泛化能力,使其能够应对不同种类的雷击故障信号。利用麻雀算法优化分类器,通过随机初始化种群、适应度计算、筛选麻雀、更新麻雀发现者与加入者、变异更新等步骤获得分类器的最优参数,并将其应用于优化后的分类器中,实现500 kV超高压直流输电线路雷击故障的自动化识别。麻雀算法作为启发式优化方法,具备自适应性与全局搜索能力,可快速在复杂搜索空间中找到最优参数,提高优化效率与识别速度。【结果】实验结果表明,去噪后信号信噪比(SNR)高于40 dB,识别均方误差(MSE)低于1.5,识别效率超过90%,平均识别时间约为2.5 s,能够准确、高效地识别500 kV超高压直流输电线路中的雷击故障。【结论】本文方法为500 kV超高压直流输电线路雷击故障的自动化识别提供了一种新技术,显著提升了识别精度和效率,为电力系统的安全稳定运行提供了有力支持。�[Objective]Transmission lines are an important component of the power system,and most line faults are caused by lightning strikes.Lightning interference identification is an important basis for ensuring the correctness of traveling wave fault analysis.To quickly identify lightning strike faults in 500 kV ultra-high-voltage direct current transmission lines and ensure the stability of the power system,an automatic identification method for lightning strike faults was proposed.[Methods]The dictionary learning algorithm was used to denoise the transmission line signal,and the minimum error objective function was established for signal amplitude fluctuation.The dictionary matrix was optimized by dictionary update through hot start and Newton iteration to obtain the denoised lightning strike fault signals of transmission lines.This effectively reduced noise interference and improved identification accuracy.The wavelet time entropy method was used to extract key features from the denoised lightning strike fault signals of transmission lines.The wavelet coefficients formed by wavelet transform were used to reconstruct the coefficients in a specific layer.A sliding time window was defined to calculate entropy and information content,and features were extracted from the transient signal of lightning current in transmission lines to provide data support for lightning strike fault identification.Different characteristic signals of lightning strikes were collected,and features were trained using ensemble learning algorithms.Multiple weak classifiers were generated and fused into a strong classifier through weights,which was used to classify each transient signal sample of lightning current.The generalization ability of the classifier was improved,and it was enabled to cope with different types of lightning strike fault signals.The classifier was optimized using the sparrow algorithm,and the optimal parameters of the classifier were obtained by randomly initializing the sparrow population,calculating fitness values,screening

关 键 词：500 kV超高压 直流输电线路 雷击故障 字典学习算法 小波时间熵 麻雀优化算法 

分 类 号：TM732[电气工程—电力系统及自动化]