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    Suzhou Electric Appliance Research Institute
    期刊號: CN32-1800/TM| ISSN1007-3175

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    基于參數(shù)自適應(yīng)DBSCAN算法的旋轉(zhuǎn)設(shè)備健康評估

    來源:電工電氣發(fā)布時間:2020-12-19 13:19 瀏覽次數(shù):685
    基于參數(shù)自適應(yīng)DBSCAN算法的旋轉(zhuǎn)設(shè)備健康評估
     
    于凱,王哲,王玉龍,董恒章,劉寶楠,張世林
    (安徽華電宿州發(fā)電有限公司,安徽 宿州 234000)
     
        摘 要:針對電廠旋轉(zhuǎn)設(shè)備的運行狀態(tài)異常檢測問題,提出一種基于參數(shù)自適應(yīng)DBSCAN算法的旋轉(zhuǎn)設(shè)備健康狀態(tài)在線評估算法。該算法中為降低人工設(shè)定鄰域半徑和密度閾值對密度聚類結(jié)果的影響,選用輪廓系數(shù)作為聚類結(jié)果有效性評價指標,基于粒子群算法(PSO)確定合理的參數(shù)值。采用參數(shù)自適應(yīng)DBSCAN算法定期對正常運行時的歷史數(shù)據(jù)進行離線聚類分析,基于此聚類結(jié)果分析實時采集的數(shù)據(jù),在線評估旋轉(zhuǎn)設(shè)備的健康指數(shù)。對某電廠旋轉(zhuǎn)設(shè)備的運行數(shù)據(jù)進行仿真分析,結(jié)果表明所提方法能夠有效檢測設(shè)備異常運行狀態(tài),為設(shè)備的安全可靠運行提供保障。
        關(guān)鍵詞:旋轉(zhuǎn)設(shè)備;健康指數(shù);參數(shù)自適應(yīng)DBSCAN算法;粒子群算法;在線評估
        中圖分類號:TM307     文獻標識碼:A     文章編號:1007-3175(2020)12-0024-06
     
    Evaluation on Health of Rotation Equipment Based on Parameter Adaptive DBSCAN Algorithm
     
    YU Kai, WANG Zhe, WANG Yu-long, DONG Heng-zhang, LIU Bao-nan, ZHANG Shi-lin
    (Anhui Huadian Suzhou Power Generation Co., Ltd, Suzhou 234000, China)
     
        Abstract: In this paper, aiming at the detection of abnormal operation status of rotating equipment in power plants, this paper proposes an online health status assessment algorithm for rotating equipment based on parameter adaptive DBSCAN algorithm. In this algorithm, in order to reduce the influence of artificially set neighborhood radius (Eps) and density threshold (MinPts) on the results of density clustering, the contour coefficient is selected as Validity evaluation index of clustering results, determine reasonable parameter values based on particle swarm optimization (PSO). The parameter adaptive DBSCAN algorithm is used to periodically perform offline clustering analysis on historical data during normal operation. Based on this clustering result, the real-time collected data is analyzed, and the health index of the rotating equipment is evaluated online. After a simulation analysis of the operating data of a rotating equipment in a power plant, the results show that the proposed method can effectively detect the abnormal operating state of the equipment and provide a guarantee for the safe and reliable operation of the equipment.
        Key words: rotation equipment; health index; parameter adaptive DBSCAN algorithm; particle swarm optimization algorithm; online evaluation
     
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