Bibliographic Details
| Title: |
Top oil temperature prediction at a multiple time scale for power transformers based on adaptive extended Kalman filter |
| Authors: |
Yingting Luo, Lei Wang, Junfei Jiang, Duanjiao Li, Shiyu Lai, Jianming Liu, Yuan La, Wenxing Sun, Mo Shi |
| Source: |
Frontiers in Energy Research, Vol 12 (2024) |
| Publisher Information: |
Frontiers Media S.A., 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:General Works |
| Subject Terms: |
Karman filter, multiple time scale prediction, top oil temperature, oil-immersed transformers, noise adaptive estimation, General Works |
| More Details: |
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 110 kV 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-min time scale and day-ahead short-term prediction on a 24-h time scale for the TOT. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2296-598X |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fenrg.2024.1440338/full; https://doaj.org/toc/2296-598X |
| DOI: |
10.3389/fenrg.2024.1440338 |
| Access URL: |
https://doaj.org/article/0b6732e0d46042f7aea8951514ca14b0 |
| Accession Number: |
edsdoj.0b6732e0d46042f7aea8951514ca14b0 |
| Database: |
Directory of Open Access Journals |
