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An intelligent hybrid technique for fault detection and condition monitoring of a thermal power plant

机译：用于火力发电厂故障检测和状态监测的智能混合技术

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This study presents an application of intelligent fault detection system for recognizing abnormal conditions during transient operation of a steam generator unit. Unobserved dynamics of evaporator section have been caused multiple false alarms and boiler emergency shut-downs. In order to detect faulty conditions, four different classifier agents were employed in parallel. The experimental data from real system performances during system trips were collected to train and validate the intelligent classifiers. The outlet results of all classifiers were combined using Yager's rule of fusion in order to improve the reliability and accuracy of fault detection process. The performances of the proposed fault detection system were evaluated during the unit's load variations and at different scenarios as one or two classifier(s) failed to detect the correct situations. The obtained results indicated the capability and feasibility of the proposed technique in preventing from raising false alarms by early detection of abnormal conditions. (C) 2018 Elsevier Inc. All rights reserved.
机译：这项研究提出了一种智能故障检测系统在识别蒸汽发生器机组瞬态运行过程中的异常情况方面的应用。蒸发器部分未观察到的动态已引起多次错误警报和锅炉紧急关闭。为了检测故障状况，并行使用了四个不同的分类器代理。收集了系统行程中来自实际系统性能的实验数据，以训练和验证智能分类器。使用Yager的融合规则对所有分类器的出口结果进行合并，以提高故障检测过程的可靠性和准确性。在一个单元的负载变化期间以及在一个或两个分类器未能检测到正确情况的情况下，在不同的情况下评估了建议的故障检测系统的性能。获得的结果表明，所提出的技术通过早期检测异常状况来防止产生误报的能力和可行性。 （C）2018 Elsevier Inc.保留所有权利。

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