A Multiarea Forecasting-Aided State Estimation Strategy for Unbalance Distribution Networks

Xu, Dongliang; Wu, Zaijun; Xu, Junjun; Zhu, Yuemin; Hu, Qinran

doi:10.1109/tii.2023.3264285

IEEE Trans. Ind. Inf.

2024

DOI: 10.1109/tii.2023.3264285

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A Multiarea Forecasting-Aided State Estimation Strategy for Unbalance Distribution Networks

Dongliang Xu¹,

Zaijun Wu²,

Junjun Xu³

et al.

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2024

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Cited by 3 publications

References 22 publications

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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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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

View full text Add to dashboard Cite

show abstract

Phased optimization of active distribution networks incorporating distributed photovoltaic storage system: A multi-objective coati optimization algorithm

Ji,

Liu,

Cheng

et al. 2024

Journal of Energy Storage

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A Levenberg–Marquardt algorithm‐based line parameters identification method for distribution network considering multisource measurement

Xu,

Song,

et al. 2024

IET Renewable Power Gen

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The uncertainty brought by the integration of distributed generations in distribution networks poses a higher demand for situation awareness in the distribution network. Accurate identification of distribution network line parameters is of great significance for the operation and control of the distribution network. This paper proposes a method for identifying distribution network line parameters considering multisource measurement. Firstly, the initial values of conductivity and susceptance are obtained through linear regression and converted into resistance and reactance, respectively. Then, based on the series parallel connection of the network end branches, a non‐linear function about resistance reactance is derived. By combining the measurement data of micro phasor measurement unit and advanced metering infrastructure at multiple times, the non‐linear measurement equation of the line is established, and the Levenberg–Marquardt algorithm is used to solve the non‐linear function, thus achieving the identification of distribution line parameters. The case study demonstrates the accuracy and effectiveness of the proposed method.

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A Multiarea Forecasting-Aided State Estimation Strategy for Unbalance Distribution Networks

Cited by 3 publications

References 22 publications

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

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

Phased optimization of active distribution networks incorporating distributed photovoltaic storage system: A multi-objective coati optimization algorithm

A Levenberg–Marquardt algorithm‐based line parameters identification method for distribution network considering multisource measurement

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