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An improvement of measurements reliability in thermal processes by application of the advanced data reconciliation method with the use of fuzzy uncertainties of measurements

机译:通过使用测量的模糊不确定性来应用高级数据对账方法,可以提高热过程中测量的可靠性

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摘要

An improvement of the measurements reliability in thermal processes by the application of the advanced data validation and reconciliation (DVR) methodology strictly depends on their assumed uncertainties. In thermal processes, which are realized in industry, the measurements uncertainties are often determined by the use of all available information concerning possible variability of measurement results. Very often, the determination of measurements uncertainties require professional knowledge, however this approach may lead to imprecise results The paper presents an application of the advanced data reconciliation method with the use of uncertainties of measurements expressed by fuzzy numbers. The proposed methodology has been presented based on example of gas-and-steam CHP unit. In order to compare calculation results for deterministic and the fuzzy uncertainties, the complex standard uncertainties of main assessment indicators of analyzed CHP unit operation supervision as well as the relative entropy of information Kullback-Leibler divergence for two multivariate normal distributions have been chosen. By the use of mentioned criterions, it has been observed that better results are obtained with the proposed method using fuzzy measurements uncertainties. The results of this study showed that calculated values of uncertainties of energy assessment indicators and the relative information entropy of a whole measurements system are both smaller and more reliable. (C) 2017 Elsevier Ltd. All rights reserved.
机译:通过使用高级数据验证和对账(DVR)方法来提高热过程中的测量可靠性,严格取决于其假定的不确定性。在工业上已经实现的热过程中,通常通过使用所有与测量结果可能变化有关的可用信息来确定测量不确定度。通常,测量不确定度的确定需要专业知识,但是这种方法可能会导致结果不准确。本文介绍了先进的数据对账方法的应用,其中使用了由模糊数表示的测量不确定度。所提出的方法已基于燃气和蒸汽CHP装置的示例进行了介绍。为了比较确定性和模糊不确定性的计算结果,选择了分析后的热电联产机组运行监督主要评估指标的复杂标准不确定性,以及两个多元正态分布的信息Kullback-Leibler散度的相对熵。通过使用提到的标准,已经观察到,所提出的方法使用模糊测量不确定性可以获得更好的结果。这项研究的结果表明,能源评估指标不确定性的计算值和整个测量系统的相对信息熵都更小且更可靠。 (C)2017 Elsevier Ltd.保留所有权利。

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