A Dissolved Gas Prediction Method for Transformer On-Load Tap Changer Oil Integrating Anomaly Detection and Deep Temporal Modeling

Qingyun Min, Zhihu Hong, Dexu Zou, Haoruo Sun, Qiwen Chen, Bohao Peng and Tong Zhao ()
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Qingyun Min: Power Science Research Institute, Yunnan Electric Power Grid Co., Ltd., Kunming 650217, China
Zhihu Hong: Power Science Research Institute, Yunnan Electric Power Grid Co., Ltd., Kunming 650217, China
Dexu Zou: Power Science Research Institute, Yunnan Electric Power Grid Co., Ltd., Kunming 650217, China
Haoruo Sun: Power Science Research Institute, Yunnan Electric Power Grid Co., Ltd., Kunming 650217, China
Qiwen Chen: Dali Power Supply Bureau, Yunnan Electric Power Grid Co., Ltd., Dali 671099, China
Bohao Peng: School of Electrical Engineering, Shandong University, Jinan 250014, China
Tong Zhao: School of Electrical Engineering, Shandong University, Jinan 250014, China

Energies, 2025, vol. 18, issue 19, 1-20

Abstract: The On-Load Tap Changer (OLTC), as a critical component of transformers, undergoes frequent switching operations that can lead to faults such as contact wear and arc discharge, which are often difficult to detect at an early stage using traditional monitoring methods. In particular, dissolved gas analysis (DGA) in OLTC oil is challenged by the unique oil gas decomposition mechanisms and the presence of background noise, making conventional DGA criteria less effective. Moreover, OLTC oil monitoring data are typically obtained through intermittent sampling, resulting in sparse time series with low resolution that complicate fault prediction. To address these challenges, this paper proposes an integrated framework combining LGOD-based anomaly detection, Locally Weighted Regression (LWR) for data repair, and the ETSformer temporal prediction model. This approach effectively identifies and corrects anomalies, restores the dynamic variation trends of gas concentrations, and enhances prediction accuracy through deep temporal modeling, thereby providing more reliable data support for OLTC state assessment and fault diagnosis. Experimental results demonstrate that the proposed method significantly improves prediction accuracy, enhances sensitivity to gas concentration evolution, and exhibits robust adaptability under both normal and fault scenarios. Furthermore, ablation experiments confirm that the observed performance gains are attributable to the complementary contributions of LGOD, LWR, and ETSformer, rather than any single component alone, highlighting the effectiveness of the integrated approach.

Keywords: on-load tap changer; dissolved gas analysis; anomaly detection; local group oscillatory difference; deep temporal prediction; ETSformer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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