基于多参量数据融合的干式空心串联电抗器匝间短路故障检测方法  

Dry-Type Air-Core Series Reactor Turn-to-Turn Short Circuit Fault Detection Method Based on Multi-Parameter Data Fusion

作　　者：王寅 王媛媛[1] 曹成军 张立志 尹有鹏 籍宏震 窦笛 WANG Yin;WANG Yuanyuan;CAO Chengjun;ZHANG Lizhi;YIN Youpeng;JI Hongzhen;DOU Di(State Key Laboratory of Disaster Prevention and Reduction for Power Grid(Changsha University of Science and Technology),Changsha 410114,China;State Grid Hunan Electric Power Co.,Ltd.Ultra High Voltage Substation Company,Changsha 410004,China)

机构地区：[1]电网防灾减灾全国重点实验室(长沙理工大学),长沙市410114 [2]国网湖南省电力有限公司超高压变电公司,长沙市410004

出　　处：《电力建设》2025年第4期16-28,共13页Electric Power Construction

基　　金：国家自然科学基金项目(52177069);国家电网科技项目(5216A321N018)。

摘　　要：【目的】针对干式空心串联电抗器微弱匝间短路故障难识别、传统方法缺乏早期预警机制的问题,提出了一种多维特征与智能算法融合的早期故障诊断方法,能够解决单一故障特征量灵敏度不足和易受噪声干扰故障漏判的问题。【方法】首先,提取并联电容器组的不平衡度、功率因数、零序电压及特征阻抗作为故障特征量,分析其在故障后各自的演变规律;其次,利用主成分分析法(principal component analysis,PCA)对原始数据降维去噪,消除干扰信息;随后,将去噪后的高饱和度特征输入至K近邻算法(K-nearest neighbor algorithm,KNN)构建故障识别和分类模型;最后,基于Maxwell建立场路耦合模型,生成单匝、轻微及多匝短路数据集,并设置无噪声与5%噪声工况验证算法的鲁棒性。【结果】仿真表明:所提方法在无噪声和5%噪声下对轻微匝间短路的识别准确率能够达到100%,且无需人工整定动作阈值。【结论】通过“特征提取-数据降噪-智能分类”三级架构,实现了微弱故障的高精度早期辨识;其创新点包括:四维特征协同提升故障灵敏性;PCA-KNN联合抗噪机制;自适应无阈值判别体系。研究成果为电力设备状态监测提供了新思路,后续仍可继续结合现场数据优化模型的泛化能力。[Objective]To address the problems of weak turn-to-turn short-circuit faults in dry-type air-core series reactors,which are difficult to recognize,and the lack of an early warning mechanism in traditional methods,this study proposes a multi-dimensional feature and intelligent algorithm fusion of an early fault diagnosis method.This method can overcome the lack of sensitivity of a single fault feature as it is easily interfered with by the noise of the fault leakage judgment.[Methods]First,the unbalance degree,power factor,zero sequence voltage,and characteristic impedance of the shunt capacitor bank are extracted as fault feature quantities,and their respective evolution laws after the fault are analyzed.Second,principal component analysis(PCA)is used to reduce the dimension and denoise the original data to eliminate interfering information.Subsequently,the denoised features with high saturation are input into the k-nearest neighbors(KNN)algorithm to construct a fault identification and classification model.Based on Maxwell,a field-circuit coupling model is established to generate single-turn,slight,and multi-turn short-circuit datasets;noise-free and 5%noise conditions are considered to verify the robustness of the algorithm.[Results]Simulation results show that the proposed method can achieve 100%recognition accuracy for minor turn-to-turn short circuits under both no noise and 5%noise,and manually adjusting the action threshold is not required.[Conclusions]This study realized high-precision early identification of weak faults through the three-stage architecture of“feature extraction-data noise reduction-intelligent classification.”The innovations include four-dimensional feature synergy to improve fault sensitivity,a PCA-KNN joint anti-noise mechanism;and an adaptive non-threshold discrimination system.The results provide a new idea for power-equipment condition monitoring,and the generalization ability of the model can be optimized by incorporating field data.

关 键 词：干式空心串联电抗器 匝间短路 MAXWELL 主成分分析 K近邻算法 

分 类 号：TM53[电气工程—电器] TM47