摘要
针对电厂旋转设备的运行状态异常检测问题,提出一种基于参数自适应DBSCAN算法的旋转设备健康状态在线评估算法。该算法中为降低人工设定邻域半径和密度阈值对密度聚类结果的影响,选用轮廓系数作为聚类结果有效性评价指标,基于粒子群算法(PSO)确定合理的参数值。采用参数自适应DBSCAN算法定期对正常运行时的历史数据进行离线聚类分析,基于此聚类结果分析实时采集的数据,在线评估旋转设备的健康指数。对某电厂旋转设备的运行数据进行仿真分析,结果表明所提方法能够有效检测设备异常运行状态,为设备的安全可靠运行提供保障。
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.
作者
于凯
王哲
王玉龙
董恒章
刘宝楠
张世林
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)
出处
《电工电气》
2020年第12期24-29,共6页
Electrotechnics Electric
关键词
旋转设备
健康指数
参数自适应DBSCAN算法
粒子群算法
在线评估
rotation equipment
health index
parameter adaptive DBSCAN algorithm
particle swarm optimization algorithm
online evaluation
