Transformer Fault Early Warning Method Based on AAKR Algorithm
Authors: 
Rongxu Hou, Yeshen He, Yiying Zhang, Zhenliu Zhou, Xueliang Wang, Ting LiAuthors Info & Claims
Rongxu Hou, Yeshen He, Yiying Zhang, Zhenliu Zhou, Xueliang Wang, Ting LiAuthors Info & ClaimsPages 127 - 133
Abstract
As the core equipment of the substation, the stable operation of the transformer is the key to the normal operation of the power system. Aiming at the high cost and difficulty of regular maintenance and inspection of transformers, this paper proposes an online monitoring model for transformers based on the Auto-Associative Kernel Regression (AAKR) algorithm. The method combines the historical health data of the substation and calculates the predicted value of the new observation vector through the AAKR algorithm. The mean squared error between the predicted and observed values is compared to a threshold Q, which yields the state of the transformer. Through the simulation test of transformer model, the validity of AAKR model in transformer condition monitoring in power system is verified. The model monitoring accuracy is around 94%.
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Index Terms
- Transformer Fault Early Warning Method Based on AAKR Algorithm
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April 2024
356 pages
ISBN:9798400718151
DOI:10.1145/3686081
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Published: 18 November 2024
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ICDSM 2024
ICDSM 2024: International Conference on Decision Science & Management
April 26 - 28, 2024
Hong Kong, China
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