A Fast and Robust State Estimator Based on Exponential Function for Power Systems

Chen, Tengpeng; Ren, He; Foo, Eddy Y. S.; Sun, Lu; Amaratunga, G.A.J.

doi:10.1109/jsen.2022.3143885

Cited by 10 publications

(5 citation statements)

References 49 publications

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“…To verify the proposed approach, simulations are performed in the IEEE 30-bus and 118-bus standard systems. The placements of PMU can be found in [36][37][38][39]. As shown in figure 2 and table 1, the standard IEEE systems are divided into multiple local areas [32].…”

Section: Simulation Resultsmentioning

confidence: 99%

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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Section: Simulation Resultsmentioning

confidence: 99%

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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“…Ho et al [36] investigate LAV, Schweppe-Huber and Hampel's three part redescending function estimators. Other MLEs use techniques like the influence function to enhance computational performance [37], or the 0 , 1 regularization to exploit the sparsity of bad data [38].…”

Section: B Related Workmentioning

confidence: 99%

Exact Modeling of Non-Gaussian Measurement Uncertainty in Distribution System State Estimation

Vanin

Acker

D’hulst

et al. 2023

IEEE Trans. Instrum. Meas.

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In power systems, state estimation is a widely investigated method to collate field measurements and power flow equations to derive the most-likely state of the observed networks. In the literature, it is commonly assumed that all measurements are characterized by zero-mean Gaussian noise. However, it has been shown that this assumption might be unacceptable, e.g., in the case of the so-called pseudo-measurements. In this paper, a state estimator is presented that can model (pseudo-)measurement uncertainty with any continuous distribution, without approximations. This is possible by reformulating state estimation as a maximum-likelihood estimation-based constrained optimization problem, in a more generic fashion than conventional implementations. To realistically describe distribution networks, three-phase unbalanced power flow equations are used. Tradeoffs between accuracy and computational effort of different uncertainty modeling methods are presented using the IEEE European Low Voltage Test Feeder.

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“…In (22), 𝐺 ̃𝐵𝐵 and 𝐹 ̃𝐵 are the global equivalent boundary information matrix and global equivalent boundary information vector respectively. In terms of solving 𝑥 𝐵 , ( 22) and ( 20) are completely equivalent.…”

Section: B Distributed Computing Mechanism Based On Equivalent Inform...mentioning

confidence: 99%

“…In the information matrix and information vector corresponding to repeated measurement, only the partition matrix 𝐺 𝐵𝐵 𝑜 and the partition vector 𝐹 𝐵𝐵 𝑜 are not zero. So, (22) needs to be modified to (28)…”

Section: B O Ib Ibmentioning

confidence: 99%

“…They can curtail the weight of bad data through nonlinear iteration and automatically suppress the influence of bad data and model deviation. In order to improve the efficiency of the cost function, a new robust state estimator based on the exponential absolute value function and the quadratic cost function is proposed for power system state estimation which obtains smaller state estimation errors under non-Gaussian measurement noise [22].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Distributed State Estimation of AC/DC System With LCC-HVDC Tie Line

Zhang,

Qi,

Wang

2023

IEEE Access

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A robust distributed state estimation algorithm for AC/DC system with AC tie lines and high voltage direct current (LCC-HVDC) tie lines is proposed. The proposed algorithm consists of robust DC state estimation in the coordinator and robust distributed AC state estimation, and implements sequential solving by exchanging coupling variables of the AC/DC system. For AC state estimation, a distributed state estimation algorithm based on the bilinear algorithm is designed, which can achieve the same accuracy as the centralized algorithm. Robust state estimator based on the exponential absolute value function and exponential square function is suitable for reducing the influence of outliers and can reduce the operation scale by simplifying the nonlinear iterative process in distributed state estimation. Finally, the correctness and effectiveness of the algorithm is verified by simulation of three IEEE 118 bus systems interconnected through AC/DC hybrid tie lines.

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A Fast and Robust State Estimator Based on Exponential Function for Power Systems

Cited by 10 publications

References 49 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

Exact Modeling of Non-Gaussian Measurement Uncertainty in Distribution System State Estimation

A Robust Distributed State Estimation of AC/DC System With LCC-HVDC Tie Line

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