Reformulation of Data Reconciliation Problem with Unknown-but-Bounded Errors

Ragot, José; Maquin, Didier

doi:10.1021/ie020213s

Cited by 10 publications

(5 citation statements)

References 22 publications

(30 reference statements)

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“…Data reconciliation, is an effective method to tune-up the measurement data [21] [5] [10] [2], which has been applied in several engineering fields [11] [3] [20] [25]. The objective of data reconciliation is to use information redundancy to handle errors in real-time measurements.…”

Section: Introductionmentioning

confidence: 99%

Data reconciliation of an open channel flow network using modal decomposition

Wu¹,

Litrico²,

Bayen³

2009

Advances in Water Resources

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This article presents a method to estimate flow variables for an open channel network governed by the linearized Saint-Venant equations and subject to periodic forcing. The discharge at the upstream end of the system and the stage at the downstream end of the system are defined as the model inputs; the flow properties at selected internal locations, as well as the other external boundary conditions, are defined as the outputs. Both inputs and outputs are affected by noise and we use the model to estimate this data. A spatially-dependent transfer matrix in the frequency domain is constructed to relate the model input and output using modal decomposition. A data reconciliation technique is used to incorporate the error in the measured data and results in a set of reconciliated external boundary conditions; subsequently, the flow properties at any location in the system can be accurately constructed from the input measurements. The applicability and effectiveness of the method is demonstrated with a case study of the river flow subject to tidal forcing in the Sacramento-San Joaquin Delta, in California. We used existing USGS sensors in place in the Delta as measurement points, and deployed our own sensors at selected locations to produce data used for the validation. The proposed method gives an accurate estimation of the flow properties at intermediate locations within the channel network.

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Section: Introductionmentioning

confidence: 99%

Data reconciliation of an open channel flow network using modal decomposition

Wu¹,

Litrico²,

Bayen³

2009

Advances in Water Resources

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“…One major disadvantage of these methods is that they lead to situations where it may be impossible to estimate all the variable by using only a subset of the remaining gross errors free measurements. Alternative approach using constraints both on the estimates and the balance residual equations has been developed for linear system (Ragot, Maquin, Adrot, 1999), (Ragot, Maquin, 2004). Johnston, Kramer (1995) established an analogy between maximum likelihood estimation and robust regression and they reported the feasibility and better performance of the robust estimators as the objective function in the data reconciliation problem when the data contain gross errors.…”

Section: Introductionmentioning

confidence: 99%

Data reconciliation: A robust approach using a contaminated distribution

Alhaj-Dibo

Maquin

Ragot

2008

Control Engineering Practice

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On-line optimisation provides a means for maintaining a process around its optimum operating range. This optimisation heavily relies on process measurements and accurate process models. However, these measurements often contain random and possibly gross errors as a result of miscalibration or failure of the measuring instruments. This paper proposes a data reconciliation strategy that deals with the presence of such gross errors. Instead of constructing the objective function to be minimized on the basis of random errors only, the proposed method takes into account both contributions from random and gross errors using a so-called contaminated Gaussian distribution. It is shown that this approach introduces less bias in the estimation due to its natural property to reject gross errors.

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“…It is imperative to minimize these measurement errors to improve the performance of our channel model. Static data reconciliation, as an effective method to tune-up the measurement data [13] [14] [15] [16], has been applied in several engineering fields [17] [18] [19]. In this article, we "reconstruct" the boundary conditions using static data reconciliation, and use them in the channel network model for an accurate simulation of the flow.…”

Section: Introductionmentioning

confidence: 99%

Data reconciliation of an open channel flow network using modal decomposition

Wu¹,

Litrico

Bayen³

2008

2008 47th IEEE Conference on Decision and Control

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This article presents a method to estimate flow variables for an open channel network governed by first-order, linear hyperbolic partial differential equations and subjected to periodic forcing. The selected external boundary conditions of the system are defined as the model input; the flow properties at internal locations, as well as the other external boundary conditions, are defined as the output. A spatially-dependent transfer matrix in the frequency domain is constructed to relate the model input and output. A data reconciliation technique efficiently eliminates the error in the measured data and results in a reconciliated external boundary conditions; subsequently, the flow properties at any location in the system can be accurately evaluated. The applicability and effectiveness of the method is substantiated with a case study of the river flow subject to tidal forcing in the Sacramento-San Joaquin Delta, California. It is shown that the proposed method gives an accurate estimation of the flow properties at any intermediate location within the channel network.

show abstract

Reformulation of Data Reconciliation Problem with Unknown-but-Bounded Errors

Cited by 10 publications

References 22 publications

Data reconciliation of an open channel flow network using modal decomposition

Data reconciliation of an open channel flow network using modal decomposition

Data reconciliation: A robust approach using a contaminated distribution

Data reconciliation of an open channel flow network using modal decomposition

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