Gross error isolability for operational data in power plants

Power plant on-line measured operational data inevitably contain random and gross errors. Data reconciliation is a data preprocessing technique, which makes use of redundant measured data to reduce the effect of random errors, and identify gross errors together with a statistical test method. When applying the data reconciliation based gross error identification method in real-life process, it is sometimes difficult to isolate a small magnitude gross error in one measurement from another due to the influence of system constraint nature and random errors. As a result, the magnitude of a gross error should satisfy a quantitative criterion to make sure of its sufficient isolation from other measurements. In this work, we propose a mathematical method to evaluate the minimum isolable magnitude for a gross error in one measurement to be isolated from another with a required probability for data reconciliation based gross error identification. We also illustrate an application of the proposed method to the feed water regenerative heating system in a 1000 MW ultra-supercritical coal-fired power generation unit. Validation of the proposed method through simulation studies is also provided, together with the influence of system constraint nature and random error standard deviations on the gross error minimum isolable magnitudes.

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