Constrained process monitoring: Moving‐horizon approach

Rao, Christopher V.; Rawlings, James B.

doi:10.1002/aic.690480111

Cited by 166 publications

(142 citation statements)

References 37 publications

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“…Condition monitoring systems that assess the WTG operational status are well developed, as well as fault detection and isolation systems that detect excessive vibration or gearbox failure; see, e.g., [43] for a review. In addition, nonlinear state estimation techniques such as MHE provide an ideal framework to tackle the detection and isolate of actuators and sensors faults [39,40], and could become the norm for WTGs in future years. These methods also have the ability to detect parameter drifts, which could be useful for monitoring blade icing or component failures, and can detect/handle sensor failure in the case that redundant measurement information is available, for instance by removing or minimizing the weight of the faulty sensor [40].…”

Section: Discussionmentioning

confidence: 99%

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Optimization‐based load reduction during rapid shutdown of multi‐megawatt wind turbine generators

Gros¹,

Chachuat

2013

Wind Energy

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This paper describes an optimization-based approach to reducing extreme structural loads during rapid or emergency shutdown of multi-megawatt wind turbine generators. The load reduction problem is cast into an optimal control formulation, and a simple, low-order model is developed in order for this optimization problem to be tractable in reasonable time using state-of-the-art numerical methods. To handle the variations in wind speed and turbulence inherent to wind turbine operation as well as the presence of model mismatch, a real-time optimization strategy based on fast sensitivity updates is also considered, whose on-line computational burden is limited to the repeated solution of quadratic programs that are designed off-line. The low-order model and both the open-loop and closed-loop optimal control strategies are validated against a high-fidelity model in the simulation environment Bladed ™ for an industrial 3MW wind turbine. Under favorable shut-down scenarios, i.e. when the wind turbine is operating properly and the actuators and sensors are not faulty, large reductions of the first compressive peak and subsequent compressive/tensile peaks of the tower load pattern are obtained at various above-rated wind speeds compared to normal pitch control shutdown. Extension to more challenging shut-down scenarios are also discussed.

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Section: Discussionmentioning

confidence: 99%

“…The tower deformation ΦT too can be estimated if tower strain gauge sensors are available, or [39,40].…”

Section: Real-time Optimal Control Of Rapid Shut-down Proceduresmentioning

confidence: 99%

Optimization‐based load reduction during rapid shutdown of multi‐megawatt wind turbine generators

Gros¹,

Chachuat

2013

Wind Energy

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“…{xt} is the sequence of estimates Xb,···, xiv, and lei = (Pei)-l (76) is a quadratic programming problem. The (xtl sequence that solves this problem gives the optimal smoothed estimate of the state given the measurements Yv ... , YN· The above discussion motivates a similar method for general nonlinear constrained estimation [25,26,44] . Given…”

Section: Moving Horizon Estimationmentioning

confidence: 99%

“…The approaches that can be used in these cases include second-order expansion of the constraints [22], the smoothly constrained Kalman filter [23], moving horizon estimation [24][25][26], various modifications of the unscented Kalman filter [2,3], interior point approaches [27], and particle filters [2, 2S]. We present an example showing that moving horizon estimation performs the best relative to estimation error, but this performance comes at a high computational expense.…”

Section: Introductionmentioning

confidence: 99%

Kalman filtering with state constraints: a survey of linear and nonlinear algorithms

Simon

2010

IET Control Theory Appl.

781

468

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The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results.

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“…Moreover, poor measurements can lead to estimates that violate the conservation laws used to model the system. In their paper, [7] have considered the EKF and MHE formulations, as a dynamic data reconciliation technique to the problem of detecting the location and magnitude of a leak in a wastewater treatment process. While the constrained estimators provide a good estimate of the total losses when there is a leak, MHE and Kalman filter provide poor estimates when there are no leaks.…”

Section: Introductionmentioning

confidence: 99%

Data treatment and analysis for on-line dynamic process optimization

Salau

Tonel

Trierweiler

et al. 2008

Computer Aided Chemical Engineering

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The filters tuning is a crucial issue due the need to quantify the accuracy of the model in terms of the process noise covariance matrix for process characterized by structural uncertainties which are time-varying. Thus, approaches to time-varying covariances were studied and included to a traditional EKF and an optimization-based state estimators constrained EKF (CEKF) formulations. The results for these approaches have shown a significant improvement in filters performance. Furthermore, the performance of these estimators as a transient data reconciliation technique has been appraised and the results have shown the CEKF suitability for this proposes.

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Constrained process monitoring: Moving‐horizon approach

Cited by 166 publications

References 37 publications

Optimization‐based load reduction during rapid shutdown of multi‐megawatt wind turbine generators

Optimization‐based load reduction during rapid shutdown of multi‐megawatt wind turbine generators

Kalman filtering with state constraints: a survey of linear and nonlinear algorithms

Data treatment and analysis for on-line dynamic process optimization

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