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作 者:李轩[1] 梅飞[2] 沙浩源 李丹奇 郑建勇[1] LI Xuan;MEI Fei;SHA Haoyuan;LI Danqi;ZHENG Jianyong(School of Electrical Engineering,Southeast University,Nanjing 210096,Jiangsu Province,China;College of Energy and Electrical Engineering,Hohai University,Nanjing 211106,Jiangsu Province,China)
机构地区:[1]东南大学电气工程学院,江苏省南京市210096 [2]河海大学能源与电气学院,江苏省南京市211106
出 处:《中国电机工程学报》2022年第4期1558-1568,共11页Proceedings of the CSEE
基 金:国家重点研发计划项目(2018YFB1500800);国网江苏省电力有限公司科技项目(J2019010)。
摘 要:针对特高压换流阀状态评估中原始数据不均衡、模型难以解释等问题,提出一种基于轻量梯度提升机(light gradient boosting machine,LightGBM)与SHAP归因分析的特高压换流阀状态评估方法。首先,通过层次聚类、自适应确定子簇规模与加权过采样生成均衡化样本,解决样本不平衡问题;接着,基于Light GBM树结构分类器构建状态评估模型,实现对样本的快速、准确评估;最后提出一种基于夏普利加法解释(SHapley Additive exPlanations,SHAP)归因理论的特高压换流阀状态评估影响因素分析框架,从全局与个体2个角度展示换流阀各状态量的重要程度及其对运行等级的影响效果。通过算例验证了所提过采样方法及状态评估模型的有效性,并通过关键影响因素的分析为换流阀状态评估结果提供依据与支撑。In order to solve the problems of imbalanced data and poor model interpretability in the state evaluation of UHVDC converter valve, a new evaluation method based on light gradient boosting machine(LightGBM) and SHAP was proposed. Firstly, balanced samples were generated through hierarchical clustering, adaptively determining sub-cluster size and weighted oversampling to solve the problem of sample imbalance. Secondly, a state evaluation model based on the LightGBM tree structure classifier was built to achieve rapid and accurate evaluation of samples. Thirdly, an influencing factors analysis framework for UHVDC converter valve state evaluation based on SHAP attribution theory was presented,which could explain the importance of each state quantity and its impact on the result from the overall and individual perspectives. Through calculation examples, this paper verified the effectiveness of the proposed oversampling method and state evaluation model. The analysis of key influencing factors provided a basis and support for the state evaluation results of the converter valve.
关 键 词:换流阀 状态评估 过采样 LightGBM SHAP值
分 类 号:TM72[电气工程—电力系统及自动化]
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