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Outliers detection method of multiple measuring points of parameters in power plant units

机译:电厂机组参数多个测量点的异常值检测方法

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摘要

A novel outlier detection method known as modified Grubbs method, which is based on median and median absolute deviation, is proposed to solve outlier detection in multiple measuring points' parameters. Weights are introduced to modify median absolute deviation and the test criterion. In the paper, a comparative study of the proposed method and the original Grubbs method in outlier detection on simulated data is presented. Due to the shortcomings of the original Grubbs method, the modified Grubbs method is a more robust alternative. The performances of the proposed method are illustrated by main steam temperature data set with and without outliers. The obtained results demonstrate that the proposed method can be used in outlier detection in thermal power plants and it is highly efficient and robust. (C) 2015 Elsevier Ltd. All rights reserved.
机译:提出了一种基于中值和中值绝对偏差的新颖异常值检测方法,称为修正格鲁布斯方法,以解决多个测量点参数中的异常值检测问题。引入权重以修改中位数绝对偏差和测试标准。本文对所提方法与原始Grubbs方法在模拟数据的离群值检测中进行了比较研究。由于原始Grubbs方法的缺点,改进的Grubbs方法是更可靠的选择。该方法的性能通过有或没有异常值的主要蒸汽温度数据集来说明。获得的结果表明,该方法可用于火力发电厂的异常值检测,并且高效,鲁棒。 (C)2015 Elsevier Ltd.保留所有权利。

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