| 徐 雄,林海军,刘悠勇,胡 边.融合 PCA 与自适应 K-Means 聚类的水电机组
故障检测在线方法[J].电子测量与仪器学报,2022,36(3):260-267 |
| 融合 PCA 与自适应 K-Means 聚类的水电机组
故障检测在线方法 |
| On-line fault detection method of hydraulic turbine combiningPCA and adaptive K-Means clustering |
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| DOI： |
| 中文关键词: 水电机组 故障在线检测 变负荷检测 自适应 K-Means 聚类 主元分析 |
| 英文关键词:hydropower unit online fault detection variable load detection adaptive K-Means clustering principal component analysis |
| 基金项目:国家自然科学基金（51775185）、湖南省自然科学基金（2018JJ2261）项目资助 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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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