摘要
高压隔离开关的“健康”状况对电网系统的安全运行至关重要,为能够有效地对隔离开关工作状况进行在线监测,该文提出了一种基于姿态传感器的隔离开关机械故障智能诊断方法,该方法包括姿态特征提取、样本处理和故障诊断3个模块。在特征提取模块,针对目前隔离开关姿态角度信息提取特征粗糙的问题,提出了基于姿态信息特点的特征提取方法。在样本处理模块,针对当前姿态信息样本库内各故障类别样本数目不平衡造成的诊断性能下降问题与特征空间中冗余、干扰信息过多问题,提出了遗传算法(genetic algorithm)优化下的少数类过采样(synthetic minority over-sampling technique)和极端随机树特征筛选(extremely randomized trees)联合样本处理方法(GA-SMOTE-ET)。该方法可以自适应增强样本库中有效信息比例,清除冗余、干扰信息的负面影响。在故障诊断模块,针对隔离开关故障诊断领域现阶段仅使用单一算法,未使用融合技术综合各算法优势的问题,提出了一种改进后的Stacking模型融合技术和两条Stacking基学习器的选取原则。该技术可以将多种学习器进行融合,实现优势互补,从而提升诊断性能。该文以GW5-35型隔离开关为样机,模拟了6种典型机械故障,使用姿态感知系统获取了隔离开关7种机械状态的姿态数据,制定了3组实验以验证所提方法的有效性,最终该方法的F1-score为0.971,显著优于传统方法。
The"health"condition of the high-voltage disconnector is very important to the safe operation of the power grid system.In order to effectively monitor the working condition of a disconnector online,this paper proposes an intelligent diagnosis method for the mechanical faults of the disconnector based on the attitude sensor.This method includes three modules:the attitude feature extraction,the sample processing and the fault diagnosis.In the feature extraction module,aiming at the problem of rough feature extraction of the disconnector attitude angle information,a feature extraction based on the characteristics of the attitude information is proposed.In the sample processing module,a GA-SMOTE-ET sample processing method is put forward to deal with the degradation of the diagnostic performance caused by the unbalanced numbers of the samples of each fault category in the current attitude information sample library and the excessive redundancy and interference information in the feature space.This method adaptively enhances the proportion of the effective information in the sample database and eliminates the negative effects of the redundant and interfering information.In the fault diagnosis module,in view of the problem that only a single algorithm is used in the field of the disconnector fault diagnosis at this stage without the fusion technology applied to integrate the advantages of each algorithm,an improved Stacking model fusion technology and two selection principles of the Stacking base learners are presented.This technology fuses multiple learning devices to achieve the complementary advantages so as to improve the diagnostic performance.Taking the GW5-35 disconnector as a prototype,this paper simulated six typical mechanical faults.The attitude sensing system is adopted to obtain the attitude data of seven mechanical states of the disconnector,and three groups of experiments are formulated to verify the effectiveness of the proposed method.Finally,the F1 score of this method is tested 0.971,significantly better than the traditional method.
作者
李可萌
陈富国
杨晖
袁欢
杨爱军
王小华
荣命哲
LI Kemeng;CHEN Fuguo;YANG Hui;YUAN Huan;YANG Aijun;WANG Xiaohua;RONG Mingzhe(School of Electrical Engineering,Xi’an Jiaotong University,Xi’an 710049,Shaanxi Province,China;Pinggao Group Co.,Ltd.,Pingdingshan 467001,Henan Province,China)
出处
《电网技术》
EI
CSCD
北大核心
2023年第9期3781-3790,共10页
Power System Technology
