Square Root Unscented Kalman Filter With Modified Measurement for Dynamic State Estimation of Power Systems

Dang, Lujuan; Wang, Wanli; Chen, Badong

doi:10.1109/tim.2022.3157005

Cited by 12 publications

(4 citation statements)

References 49 publications

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“…using ( 15)-( 19). 3) z 0 = u(x 0 ) can be obtained directly from the last column of KCT (22). Equation ( 25) can also be inverted to obtain z 0 ≈ (C h ) † zm k .…”

Section: Kkpfmentioning

confidence: 99%

“…When a system is strongly nonlinear, the EKF tends to have poor estimation accuracy and even diverges because of the inevitable linearization errors during the calculation of the Jacobian matrix. Although the UKF performs well in nonlinear systems, it cannot be used in high-dimensional systems because of the numerical stability problem [22]. Both the EKF and UKF can suffer from the curse of dimensionality, and the effect of dimensionality may become harmful in high-dimensional state-space models with state vectors of size 20 or more, as mentioned in [23], especially when there is a high degree of nonlinearities in the equations that describe the state-space model, which is exactly the case for power systems.…”

Section: Introductionmentioning

confidence: 99%

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Koopman Kalman Particle Filter for Dynamic State Estimation of Distribution System

et al. 2022

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Dynamic state estimation (DSE) plays an important role in the real-time control and monitoring of distribution systems, which are high-dimensional space-time systems. The degree of nonlinearity of distribution system increases drastically with the availability of sustainable energy and the diversification of load types, thereby reducing the accuracy of the standard linear model. Under the existing schemes, the measurement noise may not follow a Gaussian distribution. Therefore, a Koopman operator-based Kalman particle filter (KKPF) is proposed herein for estimating the dynamic states of a distribution system. The KKPF performs data-driven dynamic state estimation based on the Koopman operator theory, which does not rely on the distribution system model and can be applied to high-dimensional systems. Furthermore, this method can be applied to dynamic systems and noise with both Gaussian and non-Gaussian distributions.The KKPF method provides accurate estimation results when only a small number of measurements are available. IEEE 141 system and an improved 141 system were used to test the performance of the proposed KKPF compared to the EKF, CKF, and PF. The test revealed that the proposed KKPF can obtain accurate results under high-dimensional and non-Gaussian noise environments. INDEX TERMS Distribution system, dynamic state estimation (DSE), Koopman operator, Koopman mode decomposition, Kalman filter, particle filter.

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“…using ( 15)-( 19). 3) z 0 = u(x 0 ) can be obtained directly from the last column of KCT (22). Equation ( 25) can also be inverted to obtain z 0 ≈ (C h ) † zm k .…”

Section: Kkpfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Koopman Kalman Particle Filter for Dynamic State Estimation of Distribution System

et al. 2022

View full text Add to dashboard Cite

show abstract

“…References [6][7][8][9][10][11][12][13][14][15] focus on the robustness of state estimation. It is worth noting that while most related studies are inherently based on the assumption that state estimation noise is known and has a Gaussian behavior [16], some others, such as [17][18][19][20], consider the non-Gaussian noise and ignore the possibility of occurring disturbances. In [7], system states are estimated using a robust dynamic state estimator based on PMUs.…”

Section: Introductionmentioning

confidence: 99%

An Optimization-Based Robust Dynamic State Estimation for Power Systems with Synchronized Phasor Measurement Units, Involving Disturbance Rejection

Hakimian

Seifi

2023

International Transactions on Electrical Energy Systems

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State estimation provides the best estimate of the system states using a set of measurements that is divided into two main classes; static and dynamic types. The state estimator should provide an acceptable response in the presence of system changes and/or disturbances. To achieve this aim, considering the high rate of data changes and the high volume of exchanging information, the phasor measurement units could play an outstanding role. Furthermore, due to the inevitable errors of the measurements, and to provide an authentic estimate of the system states using the measured data, a state estimator should be able to eliminate or decrease the undesirable effect of bad data or outliers on the final output of the process. This type of state estimator is called a robust type. In this paper, a dynamic state estimator for a system with the synchronized phasor measurement units is utilized, which is capable of decreasing the effect of disturbances. Simultaneously, the robustness of the state estimation process in the presence of outliers is considered, which enables the state estimation process to reduce the negative effect of outliers on its final output. Therefore, the implemented process is called a robust dynamic state estimation method. By defining different scenarios and utilizing numerical indices, simulation results are thoroughly analyzed based on an explanatory three-bus network and the IEEE standard 14-bus and 57-bus networks using the MATPOWER tool.

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“…Considering the truncation error of EKF caused by the linearization approximation, several derivativefree DSE methodologies were designed. For example, by utilizing the square root UKF and the weighting factor acting on the measurement, [12] proposed a modified method to improve the numerical stability and robustness against measurement outliers. In [13], an adaptive UKF was introduced to simultaneously monitor the control input and status information of the integrated motor-transmission.…”

mentioning

confidence: 99%

Resilient Dynamic State Estimation for Power System Using Cauchy-Kernel-Based Maximum Correntropy Cubature Kalman Filter

Wang

Yang

Wang

et al. 2023

IEEE Trans. Instrum. Meas.

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Accurate estimation of dynamic states is the key to monitoring power system operating conditions and controlling transient stability. However, the inevitable non-Gaussian noise and randomly occurring denial-of-service (DoS) attacks may deteriorate the performance of standard filters seriously. To deal with these issues, a novel resilient cubature Kalman filter based on the Cauchy kernel maximum correntropy optimal criterion approach (termed CKMC-CKF) is developed, in which the Cauchy kernel function is utilized to describe the distance between vectors. Specifically, the errors of state and measurement in the cost function are unified by a statistical linearization technique, and the optimal estimated state is acquired by the fixed-point iteration method. Due to the salient thick-tailed feature and the insensitivity to the kernel bandwidth of Cauchy kernel function, the proposed CKMC-CKF can effectively mitigate the adverse effect of non-Gaussian noise and DoS attacks with a better numerical stability. Finally, the efficacy of the proposed method is demonstrated on the standard IEEE 39-bus system under various abnormal conditions. Compared with standard cubature Kalman filter (CKF) and maximum correntropy criterion CKF (MCC-CKF), the proposed algorithm reveals better estimation accuracy and stronger resilience.

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Square Root Unscented Kalman Filter With Modified Measurement for Dynamic State Estimation of Power Systems

Cited by 12 publications

References 49 publications

Koopman Kalman Particle Filter for Dynamic State Estimation of Distribution System

Koopman Kalman Particle Filter for Dynamic State Estimation of Distribution System

An Optimization-Based Robust Dynamic State Estimation for Power Systems with Synchronized Phasor Measurement Units, Involving Disturbance Rejection

Resilient Dynamic State Estimation for Power System Using Cauchy-Kernel-Based Maximum Correntropy Cubature Kalman Filter

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