A distributed robust state estimation algorithm for power systems considering maximum exponential absolute value

Chen, Tengpeng; Wu, Tong; Qing, Xinlin; Amaratunga, G.A.J.

doi:10.1016/j.ijepes.2021.107267

Cited by 11 publications

(10 citation statements)

References 39 publications

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“…where τ is a scalar parameter. Notice that Sii represents the ith term of diagonal matrix S [26,32], which is represented by…”

Section: Distributed Se Methods Based On Gl Functionmentioning

confidence: 99%

“…Therefore, a Gaussian-Laplace mixture model is proposed to fit the body and tails of the unknown measurement error distribution [30]. Based on the maximum likelihood criterion [31] and maximum exponential absolute value (MEAV) [32], two distributed RSE methods for power systems are proposed for large-scale power systems. The methods presented in [31,32] can deal with bad data and fairly good estimation results can be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…• A new distributed multi-area PSSE method known as distributed GL (DGL) algorithm based on the GL function and the quadratic function is implemented by using the matrix-splitting technology. The mean squared error (MSE) obtained by the proposed DGL method is less than that of the representative state estimation approaches such as WLS [13], GM [33], MS [34], distributed MLE [31] and distributed MEAV (DMEAV) [32]. • Limited information is exchanged between neighboring areas so that the privacy protection can be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

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A distributed multi-area power system state estimation method based on generalized loss function

Chen

Liu

et al. 2023

Meas. Sci. Technol.

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For power system state estimation, the measurement noise is usually assumed to follow the Gaussian distribution and the widely used estimator is the weighted least squares (WLS). However, the Gaussian distribution assumption is not always true and the performance of WLS becomes bad when the measurement noise is non-Gaussian. In this paper, a new distributed state estimation (SE) method is proposed for multi-area power systems. The proposed distributed method is based on the generalized loss function so that it can reduce the influence of non-Gaussian noise. Further, thanks to the matrix-splitting technology, the proposed distributed method can be implemented in a distributed way so that the computation time in each local area can be reduced. The simulation results carried out in the IEEE 30-bus and 118-bus systems verify the robustness and effectiveness of the proposed distributed SE method.

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“…where τ is a scalar parameter. Notice that Sii represents the ith term of diagonal matrix S [26,32], which is represented by…”

Section: Distributed Se Methods Based On Gl Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A distributed multi-area power system state estimation method based on generalized loss function

Chen

Liu

et al. 2023

Meas. Sci. Technol.

Self Cite

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to other methods based on statistics or the Kalman flter, this method has some advantages, such as the lack of dependence on system linearity and Gaussian noise, resistance to falsifed data due to the assumptions made in the formation of imperialists in the ICA method, and due to the use of predictability in the proposed method. In [11], similar to [6,13,14], the noise of measurements is assumed to be non-Gaussian. In this reference, a two-stage state estimation process is presented, the frst stage of which uses a generalized maximum probability estimator (GM estimator).…”

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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Robust Dynamic State Estimation for Power System Based on Generalized Correntropy Loss Function and Unscented Kalman Filter

Chen,

Luo,

Sun

et al. 2024

IEEJ Transactions Elec Engng

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Dynamic state estimation (DSE) of the power system is one of the most important means to ensure a safe and stable operation of the power system. However, the electromagnetic field environment, communication noise and equipment failure often lead to the appearance of non‐Gaussian noise and bad data, which ultimately cause performance degradation of traditional DSE algorithms based on the mean‐square error criterion. In this paper, a DSE method based on the generalized correntropy loss (GCL) function is proposed, which is well adapted to estimate the dynamic characteristics of the power system. The new robust DSE method can effectively deal with problems such as non‐Gaussian noise and bad data, thus improving the state estimation accuracy. Simulation results carried out on the IEEE 39‐bus system with synchronous generators demonstrate the robustness of the proposed DSE method. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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A distributed robust state estimation algorithm for power systems considering maximum exponential absolute value

Cited by 11 publications

References 39 publications

A distributed multi-area power system state estimation method based on generalized loss function

A distributed multi-area power system state estimation method based on generalized loss function

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

Robust Dynamic State Estimation for Power System Based on Generalized Correntropy Loss Function and Unscented Kalman Filter

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