Maximum Correntropy Extended Kalman Filtering for Power System Dynamic State Estimation

Mohiuddin, Sheik M.; Qi, Junjian

doi:10.1109/pesgm40551.2019.8973525

Cited by 21 publications

(9 citation statements)

References 16 publications

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“…We consider four types of additive noise: Gaussian, Laplacian, Cauchy, and mixed Gaussian/Cauchy [18]. The following additive Gaussian noise model is used [19]:…”

Section: B Noise Modelsmentioning

confidence: 99%

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Zaragoza¹,

Rao²

2022

Preprint

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<div>Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution system. However, real-world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data-driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system are not overloaded, which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We present a feature engineering pipeline that combines Singular Value Decomposition (SVD) with an Interquartile Range (IQR)-based nonlinear filter to denoise matrices of voltage magnitude measurements. We empirically show that this pipeline is effective in denoising data that contains either additive Gaussian, Laplacian, Cauchy, or mixed Gaussian/Cauchy noise. This approach reduces Frobenius error and increases the average phase identification accuracy over a year of non-stationary time series data. K-medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.</div>

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“…We consider four types of additive noise: Gaussian, Laplacian, Cauchy, and mixed Gaussian/Cauchy [18]. The following additive Gaussian noise model is used [19]:…”

Section: B Noise Modelsmentioning

confidence: 99%

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Zaragoza¹,

Rao²

2022

Preprint

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“…This approach of successively reducing the size of the Parzen windows to achieve convergence when dealing with correntropy is a widely-used method, which was firstly proposed for training mappers under correntropy and entropy cost criteria [30]. This process, sometimes referred to as "kernel annealing", has also been adopted for power system state estimation [26], [31]. The goal was to suppress the effect of gross errors in the estimation, encompassed as a particular case of non-Gaussian errors.…”

Section: Suppression Of Suspect Samples and System Transitions Through Parzen Window Adjustmentmentioning

confidence: 99%

“…New approaches have come to light, addressing the temporal aspects of the estimation problem based on the correntropy concept [25]- [27]. Reference [25] includes a quasi-steady state estimator only for supervisory control and data acquisition (SCADA) measurements, while [26] includes a dynamic state estimator only for PMUs. Reference [27] uses the generalized correntropy concept within an unscented Kalman filter to improve the robustness against outliers.…”

Section: Introductionmentioning

confidence: 99%

Tracking Power System State Evolution with Maximum-correntropy-based Extended Kalman Filter

Massignan

London

Miranda

2020

Journal of Modern Power Systems and Clean Energy

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This paper develops a novel approach to track power system state evolution based on the maximum correntropy criterion, due to its robustness against non-Gaussian errors. It includes the temporal aspects on the estimation process within a maximum-correntropy-based extended Kalman filter (MCEKF), which is able to deal with both nonlinear supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurement models. By representing the behavior of the state variables with a nonparametric model within the kernel density estimation, it is possible to include abrupt state transitions as part of the process noise with non-Gaussian characteristics. Also, a novel strategy to update the size of Parzen windows in the kernel estimation is proposed to suppress the effects of suspect samples. By properly adjusting the kernel bandwidth, the proposed MCEKF keeps its accuracy during sudden load changes and contingencies, or in the presence of bad data. Simulations with IEEE test systems and the Brazilian interconnected system are carried out. The results show that the method deals with non-Gaussian noises in both the process and measurement, and provides accurate estimates of the system state under normal and abnormal conditions.

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“…Particularly, the maximum correntropy criterion (MCC) [13,14] in information theoretic learning (ITL) [11,12] has been successfully applied in Kalman filtering to improve the robustness against impulsive noises. Typical examples include the maximum correntropy based Kalman filters [15][16][17][18][19][20][21][22][23][24], maximum correntropy based extended Kalman filters [25][26][27], maximum correntropy based unscented Kalman filters [28][29][30], maximum correntropy based square-root cubature Kalman filters [31,32] and so on. Since correntropy is a local similarity measure and insensitive to large errors, these MCC based filters are little influenced by large outliers [13,33].…”

Section: Introductionmentioning

confidence: 99%

Minimum Error Entropy Kalman Filter

Chen

Dang

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

118

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To date most linear and nonlinear Kalman filters (KFs) have been developed under the Gaussian assumption and the wellknown minimum mean square error (MMSE) criterion. In order to improve the robustness with respect to impulsive (or heavy-tailed) non-Gaussian noises, the maximum correntropy criterion (MCC) has recently been used to replace the MMSE criterion in developing several robust Kalman-type filters. To deal with more complicated non-Gaussian noises such as noises from multimodal distributions, in the present paper we develop a new Kalman-type filter, called minimum error entropy Kalman filter (MEE-KF), by using the minimum error entropy (MEE) criterion instead of the MMSE or MCC. Similar to the MCC based KFs, the proposed filter is also an online algorithm with recursive process, in which the propagation equations are used to give prior estimates of the state and covariance matrix, and a fixed-point algorithm is used to update the posterior estimates. In addition, the minimum error entropy extended Kalman filter (MEE-EKF) is also developed for performance improvement in the nonlinear situations. The high accuracy and strong robustness of MEE-KF and MEE-EKF are confirmed by experimental results.

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Maximum Correntropy Extended Kalman Filtering for Power System Dynamic State Estimation

Cited by 21 publications

References 16 publications

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Tracking Power System State Evolution with Maximum-correntropy-based Extended Kalman Filter

Minimum Error Entropy Kalman Filter

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