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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1477597
This article is part of the Research Topic Advancements in Power System Condition Monitoring, Fault Diagnosis and Environmental Compatibility View all 8 articles

Dynamic state awareness for distribution network based on robust adaptive cubature Kalman filter

Provisionally accepted
Ruihai Dai Ruihai Dai 1Yanzhen Wan Yanzhen Wan 2Deqiang Lian Deqiang Lian 2Liguo Weng Liguo Weng 2*Xiao Tang Xiao Tang 1Jianfei Wang Jianfei Wang 2
  • 1 State Grid Hangzhou Xiaoshan Power Supply Company, Hangzhou, China
  • 2 Zhejiang Zhongxin Power Engineering Construction CO., LTD, Hangzhou, Jiangsu Province, China

The final, formatted version of the article will be published soon.

    With the rapid development of the economy and society, the demand for power quality is constantly increasing. As a crucial part of grid situational awareness, distribution network state estimation plays a vital role in providing critical data support for other advanced applications, which is significant for ensuring the safe and reliable operation of the distribution network. Therefore, this paper proposes a dynamic state awareness method for distribution networks based on the robust adaptive cubature Kalman filter (RACKF). The proposed solution, grounded on the core computational concept of the cubature Kalman filter (CKF), constructs a robust noise statistical estimator (NSE) composed of a biased NSE and an unbiased NSE to adapt to the unknown and time-varying process noise parameters in the dynamic state estimation process. The proposed solution can ensure that the calculated process noise parameters always meet the constraints and guarantee the robustness of the algorithm. In addition, an estimation strategy for the fusion of multi-time scale measurement data is developed according to the RACKF-based dynamic state estimation features in order to realize system state corrections and updates. The results of simulation experiments and comparisons with traditional CKF methods demonstrate the accuracy and superiority of the proposed solution.

    Keywords: state estimation, Distribution network, Cubature Kalman filter, noise statistical estimator, robustness, Estimation strategy

    Received: 08 Aug 2024; Accepted: 07 Oct 2024.

    Copyright: © 2024 Dai, Wan, Lian, Weng, Tang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Liguo Weng, Zhejiang Zhongxin Power Engineering Construction CO., LTD, Hangzhou, Jiangsu Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.