Adaptive Sigma-Point Kalman Filtering for Wind Turbine State and Process Noise Estimation

Ritter, Bastian; Mora, Edwin; Schlicht, Thorsten; Schild, Axel; Konigorski, Ulrich

doi:10.1088/1742-6596/1037/3/032003

Cited by 9 publications

(14 citation statements)

References 19 publications

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“…A well-known drawback of MS-KF is its sensitivity with respect to the choice of two covariance matrices of artificial noises in SF. Therefore, these artificial noises should be selected carefully with those of MF [15,27]. Linear KF (LKF) is utilized for SF in most cases because there is no accurate knowledge available for the state-space model [15].…”

Section: Dual Adaptive Filtering For Measurement Noise Estimationmentioning

confidence: 99%

“…Therefore, these artificial noises should be selected carefully with those of MF [15,27]. Linear KF (LKF) is utilized for SF in most cases because there is no accurate knowledge available for the state-space model [15]. In this paper, a covariance-matching technique is utilized in the state-space model of SF because this intuitive technique can be implemented easily with low computational costs [13,15,27,33].…”

Section: Dual Adaptive Filtering For Measurement Noise Estimationmentioning

confidence: 99%

“…In Figure 2, using the equations related to the number (2) indicates that the MS-KF estimates the covariance of artificial measurements noise at every time-step of MF [13,15] (if the equations noted by the number (1) are used, MS-KF PE is performed). MS-KF ME can be conducted based on two types: "the innovation and its covariance" or "the residual and its covariance".…”

Section: Dual Adaptive Filtering For Measurement Noise Estimationmentioning

confidence: 99%

“…To address this issue, the UKF should be able to determine new artificial noises at every time step to satisfy the optimality condition. The authors thus explored several adaptive filtering techniques, which can adapt to time-varying statistical characteristics of measurements [11,[13][14][15][16][17]. Through these investigations, the dual adaptive filtering for artificial measurement noise estimation is finally selected and used along with the state-space model.…”

Section: Introductionmentioning

confidence: 99%

“…The increased number of covariance matrices of artificial noises makes it challenging to tune dual adaptive filter for various choices of those covariance matrices. It is difficult to select proper artificial noises of slave filter empirically due to the absence of accurate knowledge [13,15,18]. Therefore, it is important to construct a general framework for tuning process of dual adaptive filtering.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Regularization-Based Dual Adaptive Kalman Filter for Identification of Sudden Structural Damage Using Sparse Measurements

Lee

Song

2020

Applied Sciences

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This paper proposes a dual adaptive Kalman filter to identify parameters of a dynamic system that may experience sudden damage by a dynamic excitation such as earthquake ground motion. While various filter techniques have been utilized to estimate system’s states, parameters, input (force), or their combinations, the filter proposed in this paper focuses on tracking parameters that may change suddenly using sparse measurements. First, an advanced state-space model of parameter estimation employing a regularization technique is developed to overcome the lack of information in sparse measurements. To avoid inaccurate or biased estimation by conventional filters that use covariance matrices representing time-invariant artificial noises, this paper proposes a dual adaptive filtering, whose slave filter corrects the covariance of the artificial measurement noises in the master filter at every time-step. Since it is generally impossible to tune the proposed dual filter due to sensitivity with respect to parameters selected to describe artificial noises, particle swarm optimization (PSO) is adopted to facilitate optimal performance. Numerical investigations confirm the validity of the proposed method through comparison with other filters and emphasize the need for a thorough tuning process.

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Section: Dual Adaptive Filtering For Measurement Noise Estimationmentioning

confidence: 99%

Section: Dual Adaptive Filtering For Measurement Noise Estimationmentioning

confidence: 99%

Section: Dual Adaptive Filtering For Measurement Noise Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regularization-Based Dual Adaptive Kalman Filter for Identification of Sudden Structural Damage Using Sparse Measurements

Lee

Song

2020

Applied Sciences

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State of Charge Estimation for Lithium-Ion Battery Using Sigma-Point Kalman Filters Based on the Second Order Equivalent Circuit Model

Thuy

Van

2019

Advances in Engineering Research and Application

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Data-driven state estimation toward blade individual load-reduction of wind turbines¹

Klein

Konrad

Abel

2022

J. Phys.: Conf. Ser.

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As wind turbines tend to be build larger every year, the importance of load-reducing controls to damp the rotor blade oscillations increases. For a control algorithm to perform blade individual load-reducing strategies, online information about each rotor blade’s deflection is needed. An approach for wind turbine state estimation with special attention to the rotor blade deflections is given using a Hammerstein model driven by simulation data in a Kalman Filter (KF). The Hammerstein’s linear submodel is mainly derived as time discrete state space system via least squares, while nonlinear effects are defined in terms of their proportionality. The model noise definition required by the KF is calculated by the system model’s one-step prediction error. The simulation results show the estimate to have a standard deviation of less than 2 % in edge and 9% in flap-wise direction when using an appropriate sensor configuration. Using state-of-the-art sensors, standard deviations of less than 4% and 25% respectively are achieved. The approach is particularly attractive due to the low level of tuning required. The usage of the state estimation in a closed control loop as well as the Hammerstein model in a model-based control system offer room for promising further investigations.

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Adaptive Sigma-Point Kalman Filtering for Wind Turbine State and Process Noise Estimation

Cited by 9 publications

References 19 publications

Regularization-Based Dual Adaptive Kalman Filter for Identification of Sudden Structural Damage Using Sparse Measurements

Regularization-Based Dual Adaptive Kalman Filter for Identification of Sudden Structural Damage Using Sparse Measurements

State of Charge Estimation for Lithium-Ion Battery Using Sigma-Point Kalman Filters Based on the Second Order Equivalent Circuit Model

Data-driven state estimation toward blade individual load-reduction of wind turbines¹

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Adaptive Sigma-Point Kalman Filtering for Wind Turbine State and Process Noise Estimation

Cited by 9 publications

References 19 publications

Regularization-Based Dual Adaptive Kalman Filter for Identification of Sudden Structural Damage Using Sparse Measurements

Regularization-Based Dual Adaptive Kalman Filter for Identification of Sudden Structural Damage Using Sparse Measurements

State of Charge Estimation for Lithium-Ion Battery Using Sigma-Point Kalman Filters Based on the Second Order Equivalent Circuit Model

Data-driven state estimation toward blade individual load-reduction of wind turbines1

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Data-driven state estimation toward blade individual load-reduction of wind turbines¹