Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid Network

Shan Wang, Zhihu Hong, Qingyun Min, Dexu Zou, Yanlin Zhao, Runze Qi and Tong Zhao ()
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Shan Wang: Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650214, China
Zhihu Hong: Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650214, China
Qingyun Min: Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650214, China
Dexu Zou: Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650214, China
Yanlin Zhao: Chuxiong Power Supply Bureau, Yunnan Power Grid Co., Ltd., Chuxiong 675099, China
Runze Qi: The School of Electrical Engineering, Shandong University, Jinan 250061, China
Tong Zhao: The School of Electrical Engineering, Shandong University, Jinan 250061, China

Energies, 2025, vol. 18, issue 11, 1-21

Abstract: Accurate mechanical fault diagnosis of On-Load Tap Changers (OLTCs) remains crucial for power system reliability yet faces challenges from vibration signals’ non-stationary characteristics and limitations of conventional methods. This paper develops a hybrid framework combining metaheuristic-optimized decomposition with hierarchical temporal learning. The methodology employs a Subtraction-Average-Based Optimizer (SABO) to adaptively configure Time-Varying Filtered Empirical Mode Decomposition (TVFEMD), effectively resolving mode mixing through optimized parameter selection. The decomposed components undergo dual-stage temporal processing: A Temporal Convolutional Network (TCN) extracts multi-scale dependencies via dilated convolution architecture, followed by Gated Recurrent Unit (GRU) layers capturing dynamic temporal patterns. An experimental platform was established using a KM-type OLTC to acquire vibration signals under typical mechanical faults, subsequently constructing the dataset. Experimental validation demonstrates superior classification accuracy compared to conventional decomposition–classification approaches in distinguishing complex mechanical anomalies, achieving a classification accuracy of 96.38%. The framework achieves significant accuracy improvement over baseline methods while maintaining computational efficiency, validated through comprehensive mechanical fault simulations. This parameter-adaptive methodology demonstrates enhanced stability in signal decomposition and improved temporal feature discernment, proving particularly effective in handling non-stationary vibration signals under real operational conditions. The results establish practical viability for industrial condition monitoring applications through robust feature extraction and reliable fault pattern recognition.

Keywords: OLTC; fault diagnosis; SABO; TVFEMD; TCN; GRU (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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