The unscented Kalman filter for nonlinear estimation

Wan, Eric A.; Merwe, Rudolph van der

doi:10.1109/asspcc.2000.882463

Cited by 2,886 publications

(2,024 citation statements)

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“…Optimality is not guaranteed, and this lower-order approximation can even lead to divergence for some models. Though, the ExKF has shown success in academia and industry (Wan and Van Der Merwe, 2000).…”

Section: Extended Kalman Filter (Exkf)mentioning

confidence: 99%

“…Secondly, each particle is propagated through the nonlinear system dynamics (f , h). Thirdly, the mean and covariance of the forecasted state probability distribution is again approximated by a weighted mean of these forecasted sigma points (Wan and Van Der Merwe, 2000).…”

Section: The Unscented Kalman Filter (Ukf)mentioning

confidence: 99%

“…The UKF follows a very similar forecast and analysis update approach as the traditional KF in (10) to (17), yet applied to a finite set of particles (Wan and Van Der Merwe, 2000).…”

mentioning

confidence: 99%

“…where λ = α 2 (N + κ) − N is a scaling parameter, where α determines the spread of the particles around the mean, and κ is a secondary scaling parameter typically set to 0 (Wan and Van Der Merwe, 2000). …”

mentioning

confidence: 99%

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Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

Doekemeijer¹,

Boersma²,

Pao³

et al. 2018

Preprint

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Abstract. Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model flow dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified dynamical LES model is calibrated and used for optimization in real time. This paper presents an estimation solution with an Ensemble Kalman filter (EnKF) at its core, 5 which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Using exclusively turbine SCADA measurements, the adaptability to modeling errors and changes in atmospheric conditions (TI, wind speed) is shown. Convergence is reached within 400 seconds of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2 s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately two orders of magnitude faster. Using 10 the calibration solution presented, the surrogate model can be used for accurate forecasting and optimization.

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Section: Extended Kalman Filter (Exkf)mentioning

confidence: 99%

Section: The Unscented Kalman Filter (Ukf)mentioning

confidence: 99%

“…The UKF follows a very similar forecast and analysis update approach as the traditional KF in (10) to (17), yet applied to a finite set of particles (Wan and Van Der Merwe, 2000).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

Doekemeijer¹,

Boersma²,

Pao³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…The unscented Kalman filter (UKF) [26][27][28] as well as the unscented Rauch-Tung-Striebel smoother (URTSS) [19] address the filtering and smoothing problem by utilising the unscented transform as a way of deterministic sampling [14] in a Gaussian framework. Considering two random variables ξ and υ with a nonlinear model function υ = g(ξ), an approximation to the joint distribution can be obtained by estimating the mean µ υ and covariance Σ υυ by means of a minimal set of weighted samples.…”

Section: Unscented Transform For Filtering and Smoothingmentioning

confidence: 99%