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一种基于云模型的SF_6高压断路器状态评估方法 被引量:13

State Assessment Method for SF_6 High-voltage Circuit Breaker Based on Cloud Model
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摘要 针对SF_6高压断路器状态评估指标具有模糊性和随机性的特点,提出一种基于云模型的SF_6高压断路器整体性能模糊综合评估方法。全面建立了包含机械特性、电气特征、开断磨损、绝缘特性和其他特性等5个方面的SF_6高压断路器状态评估递阶层次评估模型,采用层次分析法和熵权法相结合的赋权方法来确定各指标的组合权重,并利用变权公式对其权重进行修正。通过云理论的数字特征处理状态参量的模糊性和随机性,从而建立评估指标状态特征和性能之间的关联。实例分析表明该方法简单易行,可为SF_6高压断路器的状态检修工作提供理论依据。 Aimed at the ambiguity and randomness of the SF6 high-voltage circuit breaker state evaluation index, a fuzzy comprehensive assessment method for the overall performance of SF6 high-voltage circuit breaker based on cloud model is proposed. The state evaluation hierarchy model of SF6 high-voltage circuit breaker, including mechanical characteristics, electrical characteristics, breaking wear, insulation characteristics and other characteristics,has been comprehensively established. Combined with the analytic hierarchy process and the entropy weight method,the combination weighting method is used to determine the combined weight of each indicator. The combined weights of the indicators are modified by the variable weight formula. The ambiguity and randomness of the state parameters are processed by the digital features of the cloud theory, which established an association between the state characteristics and performance of the evaluation indicators. The example analysis shows that the method is simple and easy. And it can provide a theoretical basis for the state maintenance of SF6 high-voltage circuit breaker.
作者 刘锦杰 叶波 罗赞琛 刘佳薇 李敏 LIU Jinjie;YE Bo;LUO Zanchen;LIU Jiawei;LI Min(China Southern Power Grid Ultra High Pressure Transmission Companies Nanning Bureau, Nanning 530000, China;Beijing Jiaotong University, Beijing 100044, China;Wuhan Zhongyuan Huadian Polytron Technologies Inc., Wuhan 430223, China)
出处 《高压电器》 CAS CSCD 北大核心 2019年第4期31-38,45,共9页 High Voltage Apparatus
基金 中国南方电网有限责任公司超高压输电公司南宁局(0103002018070202XM00001)~~
关键词 高压断路器 状态评估 层次分析法 熵权法 变权 云模型 high-voltage circuit breaker state assessment analytic hierarchy process entropy weight method variable weight cloud model
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