Pearson type VII distribution‐based robust Kalman filter under outliers interference

Peng, Yun; Wu, Panlong; He, Shuping

doi:10.1049/iet-rsn.2018.5534

Cited by 8 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…k|k and distribution-based noise modeling filter. When the DOF parameters ϕ k and σ k are fixed values and R k is also a given fixed value, the proposed algorithm becomes the GPTVM algorithm [6], which becomes GSTM when ϕ k and σ k are equal [20]. We can see that the above algorithms are all special forms of the proposed algorithm, and the algorithm proposed in this article can be applied to most cases of measurement noises.…”

Section: Measurement Updatementioning

confidence: 86%

“…as sensor failures, operational errors and strong interference, measurement data often contains a portion of distorted data that seriously deviates from the true information [4,5]. This type of measurement data is called measurement outliers, which cause the presence of heavy-tailed measurement noises (HMN) [6]. Measurement probability density function (PDF) no longer follow the Gaussian distribution, which leads to a significant decline in the estimation performance of KF [7].…”

Section: Introductionmentioning

confidence: 99%

“…This algorithm judges whether the measurement value is a normal value or an outlier through a judgment factor that obeys the Beta Bernoulli distribution, so as to determine whether the measurement is modeled as Gaussian distribution or Student's t. Subsequently, various KFs based on mixture distribution have been proposed, which all use a mixture of Gaussian and other different distributions to achieve [21][22][23]. Among them, the Gaussian and Pearson type VII (PTV) mixture distribution (GPTVM) is more adaptive to random HMN because it does not constraint that the two DOF parameters are the same [6]. Both GSTM and GPTVM can obtain good state estimation when outliers occur frequently, but their filtering accuracy is still limited by the initial values of the DOF parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Gaussian–Pearson type VII adaptive mixture distribution-based outlier-robust Kalman filter

Wang,

Wu,

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Considering that existing robust filtering algorithms rely on the selection of initial values of degree of freedom (DOF) parameters in outlier interference environments and cannot effectively cope with unknown non-stationary heavy-tailed measurement noises (HMN), aGaussian–Pearson type VII (PTV) adaptive mixture distribution-based outlier-robust Kalman filter (GPTVMAKF) is proposed. In order to determine whether the current measurement is a normal value or an outlier, a judgment factor subject to the Beta-Bernoulli distribution is introduced. PTV distribution is used to model HMN caused by outliers, and two Gamma distributions are used to model the two different DOF parameters, which can make the PTV distribution have the adaptive adjustment ability. By introducing the inverse Wishart distribution as the prior distribution of the measurement noise covariance, which is adaptively estimated to cope with the unknown time-varying measurement noises. The state and parameters are jointly estimated by variational Bayesian. Finally, the simulation experiments verify that the proposed GPTVMAKF can obtain more accurate state estimation than existing filters in the environments with varying degrees of HMN and unknown non-stationary HMN.

show abstract

Section: Measurement Updatementioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Gaussian–Pearson type VII adaptive mixture distribution-based outlier-robust Kalman filter

Wang,

Wu,

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The difference between the functional increment Δ J and the variation δ J is an infinitesimal higher order than the first‐order distance, and the variation of the function is the main linear part of the function increment. So there is the following theory [14]: If the function J [ y ( x )] reaches its extreme value on y = y ( x ), then its variation δ J on y = y ( x ) is equal to zero, which is called a variation.…”

Section: Fundermental Theorymentioning

confidence: 99%

“…This paper uses the Gini index as a contribution evaluation index [14]. In this case, VIM is represented by W (Gini) , and the Gini index is represented by G. Calculate the Gini index score W i corresponding to each eigenvalue λ i .…”

Section: Pmu Data Classification Based On Random Forestmentioning

confidence: 99%

Probabilistic stability of small disturbance in wind power system based on a variational Bayes and Lyapunov theory using PMU data

Zhang

2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

With the distribution of the large-scale emergence of wind power connected to the power system, the stability problem of small disturbance has become increasingly prominent, especially the low-frequency oscillation phenomenon caused by the weak or the negative damping. Aiming at the problem of probabilistic stability of small disturbance in power system connected with wind power, this paper proposes a method of probabilistic stability based on a variational Bayes and Lyapunov theory using PMU data. This method aims to minimize the computational efficiency when the number of wide-area time series PMU data is large. Firstly, PMU data are classified by the Random Forest Gini classification index. Secondly, a variational Bayesian framework is introduced to improve the computational efficiency. And then, to obtain the probabilistic stability criterion, this framework is combined with the direct Lyapunov theory. Finally, simulation results show that the method proposed in this paper greatly improves the system probabilistic stability of small disturbance. The comparison between the traditional Monte Carlo based on the Markov chain method and the method in this paper has been achieved, which could provide the theoretical support for the study of the low-frequency oscillation significantly.

show abstract

Robust Kalman Filter with Fading Factor Under State Transition Model Mismatch and Outliers Interference

Peng

et al. 2020

Circuits Syst Signal Process

View full text Add to dashboard Cite

Pearson type VII distribution‐based robust Kalman filter under outliers interference

Cited by 8 publications

References 24 publications

A Gaussian–Pearson type VII adaptive mixture distribution-based outlier-robust Kalman filter

A Gaussian–Pearson type VII adaptive mixture distribution-based outlier-robust Kalman filter

Probabilistic stability of small disturbance in wind power system based on a variational Bayes and Lyapunov theory using PMU data

Robust Kalman Filter with Fading Factor Under State Transition Model Mismatch and Outliers Interference

Contact Info

Product

Resources

About