中国电力 ›› 2015, Vol. 48 ›› Issue (1): 40-46.DOI: 10.11930.2015.1.40

• 发电 • 上一篇    下一篇

基于SBM技术的发电设备故障预警系统研究

滕卫明1,刘林2,卢伟明3,张震伟2,胡伯勇2   

  1. 1. 浙江省能源集团有限公司,浙江 杭州 310006;
    2. 浙江浙能技术研究院, 浙江 杭州 310008;3. 浙江浙能镇海发电有限责任公司,浙江 宁波 315200
  • 收稿日期:2014-11-01 出版日期:2015-11-25 发布日期:2015-11-24
  • 作者简介:滕卫明(1972—),男,浙江金华人,高级工程师,从事发电自动化技术应用研究、设备故障分析处理与生产技术管理工作。E-mail: 151679703@qq.com
  • 基金资助:
    浙江省能源集团科技项目资助(发电设备远程在线集中诊断系统(071119))

Study on SBM-Based Failure Prognostic System for Power Generation Equipments

TENG Weiming1, LIU Lin2, LU Weiming3, ZHANG Zhenwei2, HU Boyong2   

  1. 1. Zhejiang Energy Group Co. Ltd., Hangzhou 310006, China;
    2. Zhejiang Energy Group R&D Co. Ltd., Hangzhou 310008, China;
    3. Zhejiang Zheneng Zhenhai Electric Power Generation Co. Ltd., Ningbo 315200, China
  • Received:2014-11-01 Online:2015-11-25 Published:2015-11-24
  • Supported by:
    Science and Technology Project of Zhejiang Energy Group Co. Ltd.: Remote Online Concentrated Diagnosis System for Power Generation Equipment (071119).

摘要: 发电设备常规的监测手段均采用绝对值报警,当运行参数超过设定值时产生报警提示。这种单一的监测手段难以及时发现设备的早期征兆并对其发展趋势进行跟踪,最终导致被迫停机。为此提出基于相似性原理(SBM)的建模技术,将实际设备运行数据通过数学分析的方法,建立与实际设备相似的模型矩阵。将实际运行值与模型计算出的估计值进行比较,超过预设的偏差值即出现报警。在线展示系统参数的动态变化过程, 反映系统运行的健康状态,并及时提醒维护人员进行设备维护;此外通过实例介绍了该系统的实际运用效果。

关键词: 发电设备, 状态监测, 相似性原理(SBM), 数据挖掘, 故障预警

Abstract: Generally, the monitoring system for power generation equipments adopts absolute value to issue alarms when the operating parameters exceed the set values. This single monitoring method makes it difficult to notice the early signs of the equipment abnormalities and then keep the track of their further development, which may eventually cause the forced outage of equipments. In this paper, a modeling technique based on similarity mechanism(SBM) is proposed in which a model matrix is established to simulate the real equipments by analyzing the actual operation data with mathematical method. Then, the deviation is obtained by comparison between the actual operation value and the calculated value on the model. If it is more than the preset deviation, the alarm will be triggered. This system can display the online dynamic process of the system parameter changes, reflect how well the system is operated, and timely remind the maintenance personnel of equipment maintenance. At last, the practical applications of the system are demonstrated with case studies.

Key words: power generating equipment, state mornitoring, similarity-based modeling (SBM), data mining, failure prognostic

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