A Mechanical Fault Diagnosis Method for On-Load Tap Changers Based on GOA-Optimized FMD and Transformer
Ruifeng Wei,
Zhenjiang Chen,
Qingbo Wang,
Yongsheng Duan,
Hui Wang,
Feiming Jiang,
Daoyuan Liu and
Xiaolong Wang ()
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Ruifeng Wei: Kunming Power Supply Bureau of Yunnan Electric Grid Co., Ltd., Kunming 650011, China
Zhenjiang Chen: Kunming Power Supply Bureau of Yunnan Electric Grid Co., Ltd., Kunming 650011, China
Qingbo Wang: Kunming Power Supply Bureau of Yunnan Electric Grid Co., Ltd., Kunming 650011, China
Yongsheng Duan: Kunming Power Supply Bureau of Yunnan Electric Grid Co., Ltd., Kunming 650011, China
Hui Wang: Kunming Power Supply Bureau of Yunnan Electric Grid Co., Ltd., Kunming 650011, China
Feiming Jiang: Kunming Power Supply Bureau of Yunnan Electric Grid Co., Ltd., Kunming 650011, China
Daoyuan Liu: School of Electrical Engineering, Shangdong University, Jinan 250100, China
Xiaolong Wang: School of Electrical Engineering, Shangdong University, Jinan 250100, China
Energies, 2025, vol. 18, issue 14, 1-24
Abstract:
Mechanical failures frequently occur in On-Load Tap Changers (OLTCs) during operation, potentially compromising the reliability and stability of power systems. The goal of this study is to develop an intelligent and accurate diagnostic approach for OLTC mechanical fault identification, particularly under the challenge of non-stationary vibration signals. To achieve this, a novel hybrid method is proposed that integrates the Gazelle Optimization Algorithm (GOA), Feature Mode Decomposition (FMD), and a Transformer-based classification model. Specifically, GOA is employed to automatically optimize key FMD parameters, including the number of filters (K), filter length (L), and number of decomposition modes (N), enabling high-resolution signal decomposition. From the resulting intrinsic mode functions (IMFs), statistical time domain features—peak factor, impulse factor, waveform factor, and clearance factor—are extracted to form feature vectors. After feature extraction, the resulting vectors are utilized by a Transformer to classify fault types. Benchmark comparisons with other decomposition and learning approaches highlight the enhanced performance of the proposed framework. The model achieves a 95.83% classification accuracy on the test set and an average of 96.7% under five-fold cross-validation, demonstrating excellent accuracy and generalization. What distinguishes this research is its incorporation of a GOA–FMD and a Transformer-based attention mechanism for pattern recognition into a unified and efficient diagnostic framework. With its high effectiveness and adaptability, the proposed framework shows great promise for real-world applications in the smart fault monitoring of power systems.
Keywords: on-load tap changer; mechanical fault diagnosis; feature mode decomposition; gazelle optimization algorithm; transformer; vibration signal analysis; time-domain feature extraction (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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