Keywords
transformer fault diagnosis, neighborhood rough set, improved particle swarm algorithm, extreme learning machine
Abstract
The accuracy of the intelligent transformer fault diagnosis method based on the DGA data is easily affected by the input characteristics, and the parametersof the extreme learning machine model is difficult to select. Thus, a transformer fault diagnosis method based on the neighborhood rough set and the adaptive mutation particle swarm extreme learning machine algorithm is proposed. Firstly, the initial feature set of transformer faults is established based on the various DGA fault diagnosis standards, and the key feature indicators with higherimportance according to the neighborhood rough set analysis.Secondly, when optimizing the parameters of the extreme learning machine on the basis ofthe particle swarm algorithm, it is easy to be premature and fall into the local maximum. Hence, an improved particle swarm optimization algorithm with premature self-check mutation mechanism is proposed.Finally, through a case study, the proposed methodis compared with the IEC three-ratio method and the different combinations of extreme learning machines,which verifies that the better diagnosis accuracy of the proposed method.
DOI
10.19781/j.issn.1673-9140.2022.03.019
First Page
157
Last Page
164
Recommended Citation
ZHOU, Xiu; YI, Kai; LI, Gang; TIAN, Tian; and YANG, Xin
(2022)
"Atransformer DGA fault diagnosis approachbased on neighborhood rough set and AMPSO-ELM,"
Journal of Electric Power Science and Technology: Vol. 37:
Iss.
3, Article 19.
DOI: 10.19781/j.issn.1673-9140.2022.03.019
Available at:
https://jepst.researchcommons.org/journal/vol37/iss3/19