In coal‐fired power plants, the feed water treatment processes (FWTPs) supply qualified water to utility boilers. The faults of an FWTP may endanger the whole power plant. The classical PCA‐/KPCA‐based fault detection algorithms are valid mainly under single operation condition. When they are put to a practical FWTP, they cannot deal with the problems as treatment route switching, multiple operation conditions, process fluctuations, and process nonlinearity. In this paper, k‐means, wavelet denoise (WD), and KPCA are integrated together to form a new algorithm as K‐WD‐KPCA. It is expected to deal with the process nonlinearity through KPCA, cope with the multiple operation conditions through k‐means, and relieve the process fluctuations through WD. In the experiments on the data sets collected from a practical FWTP, the multiple operation conditions are classified into three categories; consequently, three KPCA models are trained and correspondingly scheduled for online condition matching. WD is further used to denoise the real‐time T² and SPE statistics. Results show that the WD part of K‐WD‐KPCA algorithm can indeed lower the false alarm rate without reducing its fault detection performance. Finally, the proposed K‐WD‐KPCA algorithm is coded into a software platform and deployed to a coal‐fired power plant containing 2 × 1000 MW generation units. The effectiveness of the K‐WD‐KPCA algorithm is convinced through field application results.

中文翻译：

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考虑多种运行条件，使用K‐WD‐KPCA对燃煤电厂FWTP进行故障检测

在燃煤电厂中，给水处理过程（FWTP）向公用事业锅炉提供合格的水。FWTP的故障可能危及整个发电厂。基于PCA / KPCA的经典故障检测算法主要在单个操作条件下有效。当将它们用于实际的FWTP时，它们将无法处理处理路径切换，多种操作条件，过程波动和过程非线性等问题。本文将k-means，小波降噪（WD）和KPCA集成在一起，形成了一种新算法，即K-WD-KPCA。期望通过KPCA处理过程非线性，通过k-means处理多种运行条件，并通过WD减轻过程波动。在对从实际FWTP收集的数据集进行的实验中，多种运行条件分为三类；因此，对三个KPCA模型进行了训练，并相应地安排了在线条件匹配的时间。WD进一步用于对实时信号进行降噪T ²和SPE统计信息。结果表明，K‐WD‐KPCA算法的WD部分确实可以降低误报率，而不会降低其故障检测性能。最后，将提出的K‐WD‐KPCA算法编码到软件平台中，并部署到包含2 × 1000 MW发电机组的燃煤电厂中。通过现场应用结果证明了K‐WD‐KPCA算法的有效性。