Partial Discharge Type Identification of 10 kV T-Type Terminal Based on Empirical Mode Decomposition and Deep Convolution Neural Network
Source abstract
Abstract
As a relatively weak part of cable insulation, T-type cable terminals will have insulation defects due to process, installation, and other problems, resulting in partial discharge. Therefore, this paper uses Deep Convolution Neural Network (DCNN) and Empirical Mode Decomposition (EMD) to identify the partial discharge type of a 10 kV T-type cable terminal. This method uses the partial discharge experimental platform of the T-type cable terminal to collect the partial discharge signal. After the original signal that is difficult to identify is decomposed by EMD, a series of intrinsic mode components (IMFs) that are easy to locate are obtained. The deep learning network model is used to identify the defect type of the IMF signal. The results show that the overall defect recognition rate of this method reaches 95.3%. Compared with the traditional random forest algorithm (RF), the 10 kV T-type terminal partial discharge type recognition method based on EMD–DCNN is considered in this paper. The recognition accuracy of the main insulation scratch, bushing fouling, and joint loosening defects is higher than that of the traditional mechanical learning algorithm, RF, indicating that the method adopted in this paper can more effectively and accurately identify the defect type.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Control Systems · Advanced Algorithms and Applications · Smart Grid and Power Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical and New Energy, Three Gorges University, Yichang 443002, China
References (29)
Show source-provided references
1. Condition monitoring and diagnosis of power equipment: Review and prospective10.1049/hve.2017.0026 ↗
2. Partial discharge detection and diagnosis in gas insulated switchgear: State of the art10.1109/MEI.2019.8735667 ↗
3. Feature extraction and ageing state recognition using partial discharges in cables under HVDC10.1016/j.epsr.2019.106053 ↗
4. Zhou, H., Wan, D., Zhao, M., Fang, J., Zhou, W., and Peng, S. (2019, January 8–10). Signal Recognition Method of Power Cable Oscillating Wave Partial Discharge Detection Based on Support Vector Machine. Proceedings of the 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), Changsha, China.10.1109/EI247390.2019.9061706 ↗
5. Dong, Z., Bao, F., Hu, X., Zeng, H., Wu, Y., and Chen, Q. (2023, January 1–3). Cable Fault Identification Method Based on mRMR and Optimized Convolutional Neural Network. Proceedings of the 2023 6th International Conference on Electronics and Electrical Engineering Technology (EEET), Nanjing, China.10.1109/EEET61723.2023.00016 ↗
6. Identification of partial discharge defects based on deep learning method10.1109/TPWRD.2019.2910583 ↗
7. Random forest based optimal feature selection for partial discharge pattern recognition in HV cables10.1109/TPWRD.2019.2918316 ↗
8. A convolutional neural network-based deep learning methodology for recognition of partial discharge patterns from high-voltage cables10.1109/TPWRD.2019.2906086 ↗
9. Discharge signal Spectrum of Typical Office discharge Model of Power transformer
10. Robust Feature Extraction and Classification of Acoustic Partial Discharge Signals Corrupted with Noise10.1109/TIM.2016.2639678 ↗
11. High Noise Tolerance Feature Extraction for Partial Discharge Classification in XLPE Cable Joints10.1109/TDEI.2016.005864 ↗
12. Huang, X., Xiong, J., Zhang, Y., and Liang, J. (2020). China Electric Power. Partial Discharge Pattern Recognition of Switchgear Based on Residual Convolutional Neural Network, IOP Publishing Ltd.10.1088/1742-6596/1659/1/012057 ↗
13. Mao, Z., Li, F., Ye, Y., Wang, P., and Yuan, Q. (2019). Pattern recognition of cable partial discharge type based on BP neural network algorithm. Mech. Electr. Inf., 20–22.
14. Partial discharge pattern recognition by neuro-fuzzy networks in heat-shrinkable joints and terminations of XLPE insulated distribution cables10.1109/TPWRD.2006.875861 ↗
15. Fault Diagnosis of Cylinder liner and piston Ring of Marine Diesel Engine based on improved Eemd-Mb1dcnn
16. Aloysius, N., and Geetha, M. (2017, January 6–8). A Review on Deep Convolutional Neural Networks. Proceedings of the 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, India.10.1109/ICCSP.2017.8286426 ↗
17. Convolutional neural network: A review of models methodologies and applications to object detection10.1007/s13748-019-00203-0 ↗
18. Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K. (2017, January 21–26). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.634 ↗
19. Liu, Q., Zhang, N., Yang, W., Wang, S., Cui, Z., Chen, X., and Chen, L. (2017, January 28). A Review of Image Recognition with Deep Convolutional Neural Network. Proceedings of the International Conference Intelligence Computer, Odisha, India.10.1007/978-3-319-63309-1_7 ↗
20. Image recognition and classification algorithm based on AlexNet
21. Recognition and Analysis of Transformer UHF Partial Discharge Signals based on Statistical Characteristics
22. Wang, J., Fang, C., Gao, G., and Cheng, H. (2024). Local discharge type identification of 10kV T-type terminal based on VMD-WVD and AlexNet. J. Wuhan Univ., 1–9.
23. Improvement of EMD algorithm and its application in Signal denoising
24. Yuan, C. (2024). Mechanical Fault Diagnosis of vacuum Circuit breaker in Distribution Network based on EMD and neural Network. China Mach., 96–98.
25. Li, X., Shen, L., Tian, Y., Yin, J., Wang, L., Yang, D., Mu, L., and Zhu, Y. (2024). Research on intelligent coal and rock identification method based on EMD and DCNN. Coal Mine Mach., 45.
26. Application of deep learning in image recognition
27. Transformer fault prediction based on deep learning network characteristics under extreme weather conditions
28. Research on Transmission line fault prediction Method based on Deep learning
29. Research and Design of Rogowski Coil for Partial Discharge Signal Measurement