Skip to main content

ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1440338
This article is part of the Research Topic Enhancing Resilience in Smart Grids: Cyber-Physical Systems Security, Simulations, and Adaptive Defense Strategies View all 3 articles

Top Oil Temperature Prediction at a Multiple Time Scale for Power Transformers Based on Adaptive Extended Kalman Filter

Provisionally accepted
Yingting Luo Yingting Luo *Lei Wang Lei Wang *Junfei Jiang Junfei Jiang *Duanjiao Li Duanjiao Li Shiyu Lai Shiyu Lai *Jianming Liu Jianming Liu *Yuan La Yuan La *Wenxing Sun Wenxing Sun *Mo Shi Mo Shi *
  • Electric Power Research Institute of Guangdong Power Grid Co., Ltd.,, Guangzhou, China

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

    To achieve load management optimization and timely failure warning for power transformers, as well as improve the reliability of the power network, this paper proposes a multiple time scale prediction method for top oil temperature (TOT) based on an adaptive extended Kalman filter (AEKF) algorithm. This method combines the Kalman filter (KF) algorithm and the D.Susa thermal model. The TOT, oil exponent and oil time constant are taken as state variables, while the ambient temperature and load current are used as input variables. The iterative optimization of the oil exponent and oil time constant is realized by comparing the estimated and observed TOT values. Moreover, the proposed method utilizes an adaptive noise estimator to correct the noise statistics parameters, which simplifies the initial noise setting and thus further improves the TOT prediction accuracy. A case study is conducted with two 110kV transformers. The results show that comparing the thermal equivalent circuit model and the extended KF algorithm, the proposed method has a higher accuracy in the intraday ultra-short-term prediction on a 15-minute time scale and day-ahead short-term prediction on a 24-hour time scale for the TOT.

    Keywords: Karman filter, multiple time scale prediction, Top Oil Temperature, oil-immersed transformers, noise adaptive estimation 1

    Received: 29 May 2024; Accepted: 22 Jul 2024.

    Copyright: © 2024 Luo, Wang, Jiang, Li, Lai, Liu, La, Sun and Shi. 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:
    Yingting Luo, Electric Power Research Institute of Guangdong Power Grid Co., Ltd.,, Guangzhou, China
    Lei Wang, Electric Power Research Institute of Guangdong Power Grid Co., Ltd.,, Guangzhou, China
    Junfei Jiang, Electric Power Research Institute of Guangdong Power Grid Co., Ltd.,, Guangzhou, China
    Shiyu Lai, Electric Power Research Institute of Guangdong Power Grid Co., Ltd.,, Guangzhou, China
    Jianming Liu, Electric Power Research Institute of Guangdong Power Grid Co., Ltd.,, Guangzhou, China
    Yuan La, Electric Power Research Institute of Guangdong Power Grid Co., Ltd.,, Guangzhou, China
    Wenxing Sun, Electric Power Research Institute of Guangdong Power Grid Co., Ltd.,, Guangzhou, China
    Mo Shi, Electric Power Research Institute of Guangdong Power Grid Co., Ltd.,, Guangzhou, 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.