融合PCA与自适应K-Means聚类的水电机组故障检测在线方法
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1.湖南师范大学;2.湖南大学;3.胡南五凌电力科技有限公司

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TP206

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On-line fault detection method of hydraulic turbine combining PCA and adaptive K-Means clustering
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    摘要:

    灯泡贯流式水电机组在运行过程中,由于受水力因素、机械、工况等因素影响,很容易导致转轮叶片与转轮室发生故障,严重影响水电机组安全运行。在分析灯泡贯流式水电机组转轮叶片与转轮室故障信号特征的基础上,提出了一种基于K均值(K-Means)和莱特准则(Wright"s criterion)的水电机组故障在线检测方法。该方法利用主元分析(PCA)对水电机组振动和噪声信号特征降维后,融合莱特准则改进传统K均值算法,以实现K值的自适应选择,对特征进行在线聚类,能快速准确识别水轮机变负荷状态与金属扫膛故障。将本文提出的这种方法应用到五凌电力近尾洲水电站灯泡贯流式机组故障检测中,实验结果表明,采用该方法的故障在线检测准确率为100%、变负荷在线检测准确率为96.7%,运行近10个月没有出现故障误报和漏报,表明了该方法的有效性。

    Abstract:

    During the operation of the bulb tubular hydropower unit, due to hydraulic factors, machinery, working conditions and other factors, it is easy to cause the runner blades and runner chamber to malfunction, which seriously affects the safe operation of the hydropower unit. Based on the analysis of the fault signal characteristics of the runner blades and runner chamber of the bulb tubular hydropower unit, an online fault detection method for hydropower units based on K-Means and Wright"s criterion is proposed. This method uses principal component analysis (PCA) to reduce the dimensionality of the vibration and noise signal characteristics of the hydropower unit, and integrates the Wright criterion to improve the traditional K-means algorithm to realize the adaptive selection of the K value, and perform online clustering of the features, which can quickly and accurately identify .The variable load state of the turbine and the failure of the metal sweeping chamber. The method proposed in this paper is applied to the fault detection of the bulb tubular unit of Wuling Electric Power’s Jinweizhou Hydropower Station. The experimental results show that the accuracy of the online fault detection using this method is 100% and the accuracy of the variable load online detection is 96.7. %, there has been no fault false positives and false negatives in the past 10 months of operation, indicating the effectiveness of the method.

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  • 收稿日期:2021-08-09
  • 最后修改日期:2021-10-01
  • 录用日期:2021-10-19
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