An Adaptive Kalman Filter for Dynamic Harmonic State Estimation and Harmonic Injection Tracking

Yu, Kuo-Nan; Watson, N.R.; Arrillaga, J.

doi:10.1109/tpwrd.2004.838643

Cited by 189 publications

(101 citation statements)

References 10 publications

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“…Extended Kalman filters, as proposed in (Kumar et al, 2006), can be applied to a certain power system bus in order to measure active and reactive powers. (Yu et al, 2005) analyses the dynamical response of Kalman filters during power system transients and proposes an Adaptive Kalman filter in order to improve the performance of the estimation process. In case of (Macías & Exposito, 2006), such problem is solved by applying a selftuning algorithm to the covariance matrix.…”

Section: Application Of Kalman Filters To Electrical Power Systemsmentioning

confidence: 99%

Estimation of Electrical Power Quantities by Means of Kalman Filtering

Pigazo¹,

Moreno²

2009

Kalman Filter Recent Advances and Applications

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Section: Application Of Kalman Filters To Electrical Power Systemsmentioning

confidence: 99%

Estimation of Electrical Power Quantities by Means of Kalman Filtering

Pigazo¹,

Moreno²

2009

Kalman Filter Recent Advances and Applications

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“…For the optimal choice of the noise covariance matrices, various improved KF algorithms have been applied to power system harmonic state estimation. For example, considering the uncertainty of the process noise covariance matrix, Yu [10] proposed an adaptive Kalman filter method in which two basic Q models can be switched for steadystate and transient estimation. As a comparison, Shih and Huang [11] adjusted the measurement noise parameter R instead of Q to increase the robustness of the EKF method.…”

Section: Introductionmentioning

confidence: 99%

Detection of Voltage Sag using An Adaptive Extended Kalman Filter Based on Maximum Likelihood

Yang¹,

Li²,

Zhang³

et al. 2017

Journal of Electrical Engineering and Technology

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-An adaptive extended Kalman filter based on the maximum likelihood (EKF-ML) is proposed for detecting voltage sag in this paper. Considering that the choice of the process and measurement error covariance matrices affects seriously the performance of the extended Kalman filter (EKF), the EKF-ML method uses the maximum likelihood method to adaptively optimize the error covariance matrices and the initial conditions. This can ensure that the EKF has better accuracy and faster convergence for estimating the voltage amplitude (states). Moreover, without more complexity, the EKF-ML algorithm is almost as simple as the conventional EKF, but it has better anti-disturbance performance and more accuracy in detection of the voltage sag. More importantly, the EKF-ML algorithm is capable of accurately estimating the noise parameters and is robust against various noise levels. Simulation results show that the proposed method performs with a fast dynamic and tracking response, when voltage signals contain harmonics or a pulse and are jointly embedded in an unknown measurement noise.

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“…Harmonic State Estimation (HSE) [1,2] involves harmonic distribution and harmonic source identification [3][4][5][6][7][8][9][10]. With regard to the HSE problems, a multitude of methods have been employed, including least squares (LS) [1,2], singular value decomposition (SVD) [4], Kalman filter [5], neural networks (NN) [6], sparsity maximization [7], and particle swarm optimization (PSO) [8]. Additionally, it has been proved that the complete harmonic distribution can be derived based on selected measurement data [5].…”

Section: Introductionmentioning

confidence: 99%

“…With regard to the HSE problems, a multitude of methods have been employed, including least squares (LS) [1,2], singular value decomposition (SVD) [4], Kalman filter [5], neural networks (NN) [6], sparsity maximization [7], and particle swarm optimization (PSO) [8]. Additionally, it has been proved that the complete harmonic distribution can be derived based on selected measurement data [5]. However, in terms of the HSE methods, precise information on network parameters are required, which are rarely known in practice.…”

Section: Introductionmentioning

confidence: 99%

Harmonic Source Localization Approach Based on Fast Kernel Entropy Optimization ICA and Minimum Conditional Entropy

Zang

et al. 2016

Entropy

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Based on the fast kernel entropy optimization independent component analysis and the minimum conditional entropy, this paper proposes a harmonic source localization method which aims at accurately estimating harmonic currents and identifying harmonic sources. The injected harmonic currents are estimated by the fast kernel entropy optimization independent component analysis (FKEO-ICA) in the absence of prior knowledge of harmonic impedances. Then, the minimum conditional entropy is applied to locate the harmonic sources based on the estimated harmonic currents. The proposed harmonic source localization method is validated on the IEEE 34-bus system. By applying the correlation coefficient and three error evaluation indicators, comparison has been made among the performances of the FKEO-ICA and three other ICA algorithms. The results show that the FKEO-ICA algorithm could achieve a significantly better accuracy of harmonic current estimation, while the minimum conditional entropy could determine the locations of harmonic sources precisely.

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An Adaptive Kalman Filter for Dynamic Harmonic State Estimation and Harmonic Injection Tracking

Cited by 189 publications

References 10 publications

Estimation of Electrical Power Quantities by Means of Kalman Filtering

Estimation of Electrical Power Quantities by Means of Kalman Filtering

Detection of Voltage Sag using An Adaptive Extended Kalman Filter Based on Maximum Likelihood

Harmonic Source Localization Approach Based on Fast Kernel Entropy Optimization ICA and Minimum Conditional Entropy

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