摘要
油中溶解气体分析对变压器故障预警及诊断具有重要意义。针对油中溶解气体特征量种类众多、故障关联特征分析不足等问题,文中以油浸式变压器为研究对象,提出了基于油中溶解气体特征量筛选的变压器故障诊断方法。首先,对油中溶解气体的原始特征量进行特征衍生,通过随机森林(random forest,RF)计算特征量对故障诊断的重要度,筛选得到最佳特征组合。其次,采用树结构概率密度估计(tree-structured parzen estimator,TPE)实现RF模型的参数寻优,并形成TPE-RF诊断模型。同时,结合多种评价指标,证明所提方法能够对变压器作出准确的故障诊断。最后,提出TreeSAHP模型分析特征量对各故障的重要度,优选出各故障关联的主要特征量,并根据变压器运行案例,探讨了该方法在电力行业现场应用中的适用性,验证了该方法的有效性。
Dissolved gas analysis is important for the early warning and diagnosis of transformer faults.Aiming at the problems of numerous types of features for dissolved gas in oil and the insufficient analysis of fault associated features,a new fault diagnosis method for oil-immersed transformers based on feature selection of dissolved gas in oil is proposed.Firstly,the derivation of original features for dissolved gases is completed.The optimal combination of features is selected by calculating the importance of features for fault diagnosis based on random forest(RF).Then,the tree-structured parzen estimator(TPE)is used to realize the parameter optimization of the RF model,and the TPE-RF diagnostic model is obtained.Combined with the various evaluated indicators,the proposed method is proved to be able to diagnosis the transformer faults accurately.Finally,the TreeSHAP model is introduced to analyze the importance of the features corresponding to each fault,and the specialized features for each fault are selected.According to the case of transformer in operation,the applicability of the method in the power system is discussed,and the effectiveness of the method is verified.
作者
廖才波
杨金鑫
胡雄
邱志斌
刘小天
朱文清
LIAO Caibo;YANG Jinxin;HU Xiong;QIU Zhibin;LIU Xiaotian;ZHU Wenqing(School of Information Engineering,Nanchang University,Nanchang 330031,China)
出处
《电力工程技术》
北大核心
2024年第1期192-200,共9页
Electric Power Engineering Technology
基金
国家自然科学基金资助项目(62163025)
江西省自然科学基金资助项目(20212ACB212007)。
