Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid Network
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-optimiz...
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MDPI AG
2025-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/11/2934 |
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| author | Shan Wang Zhihu Hong Qingyun Min Dexu Zou Yanlin Zhao Runze Qi Tong Zhao |
| author_facet | Shan Wang Zhihu Hong Qingyun Min Dexu Zou Yanlin Zhao Runze Qi Tong Zhao |
| author_sort | Shan Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-59cbb44372c64cab9d25bdad486c8663 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-59cbb44372c64cab9d25bdad486c86632025-08-20T03:11:18ZengMDPI AGEnergies1996-10732025-06-011811293410.3390/en18112934Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid NetworkShan Wang0Zhihu Hong1Qingyun Min2Dexu Zou3Yanlin Zhao4Runze Qi5Tong Zhao6Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650214, ChinaElectric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650214, ChinaElectric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650214, ChinaElectric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650214, ChinaChuxiong Power Supply Bureau, Yunnan Power Grid Co., Ltd., Chuxiong 675099, ChinaThe School of Electrical Engineering, Shandong University, Jinan 250061, ChinaThe School of Electrical Engineering, Shandong University, Jinan 250061, ChinaAccurate 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.https://www.mdpi.com/1996-1073/18/11/2934OLTCfault diagnosisSABOTVFEMDTCNGRU |
| spellingShingle | Shan Wang Zhihu Hong Qingyun Min Dexu Zou Yanlin Zhao Runze Qi Tong Zhao Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid Network Energies OLTC fault diagnosis SABO TVFEMD TCN GRU |
| title | Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid Network |
| title_full | Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid Network |
| title_fullStr | Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid Network |
| title_full_unstemmed | Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid Network |
| title_short | Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid Network |
| title_sort | diagnosis of power transformer on load tap changer mechanical faults based on sabo optimized tvfemd and tcn gru hybrid network |
| topic | OLTC fault diagnosis SABO TVFEMD TCN GRU |
| url | https://www.mdpi.com/1996-1073/18/11/2934 |
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