Circuit Breaker Fault Detection Based on CEEMD-GSA-SVM

DOI: https://doi.org/10.62517/jes.202502109

Author(s)

Zhaohui Liu

Affiliation(s)

Inner Mongolia Power (Group) Co., Ltd, Hohhot Power Supply Branch, Hohhot, Inner Mongolia, China

Abstract

To accurately extract the fault characteristics of circuit breakers, a fault detection method based on comprehensive sensitive empirical mode decomposition (CEEMD), gravitational search algorithm (GSA) and support vector machine (SVM) is proposed. Firstly, the CEEMD is used to process the circuit breaker signal, and using Hilbert transform to establish the marginal spectrum of the obtained components. Select energy entropy as the feature vector. Aiming at the problem that the parameter setting in SVM method affects the classification performance, using GSA to determine SVM parameters. Finally, Kernel-based Fuzzy C-Means (KFCM)- SVM is used for fault detection. In the end, the fault detection accuracy of the CEEMD-GSA-SVM method is 97.5%.

Keywords

Complementary Ensemble Empirical Mode Decomposition; Gravitational Search Algorithm; Circuit Breaker; Fault Detection; Support Vector Machine

References

[1]Amir Khan U, Lee J G, Amir F, et al. A Novel Model of HVDC Hybrid-Type Superconducting Circuit Breaker and Its Performance Analysis for Limiting and Breaking DC Fault Currents. IEEE Transactions on Applied Superconductivity, 2015, 25(6): 1-9. DOI:10.1109/TASC.2015.2477373. [2]Dai Y, Zhang M, Fan K, et al. Investigation of Series-Connected IGBTs in Fast High-Voltage Circuit Breaker. Journal of Fusion Energy, 2015, 34(6): 1406-1410. DOI:10.1007/s10894-015-9981-9. [3]Mengyuan H, Qiaolin D, Shutao, et al. Research of Circuit Breaker Intelligent Fault Diagnosis Method Based on Double Clustering. Ieice Electronics Express, 2017, 14(17): 20170463-20170463. DOI:10.1587/elex.14.20170463. [4]Longjiang D, Shuting, Changgeng Z. Application of Multiscale Entropy in Mechanical Fault Diagnosis of High Voltage Circuit Breaker. Entropy, 2018, 20(5): 325. DOI:10.3390/e20050325. [5]Zhao S, Wang E, Hao J. Fault diagnosis method for energy storage mechanism of high voltage circuit breaker based on CNN characteristic matrix constructed by sound-vibration signal. Journal of Vibroengineering, 2019(6). DOI:10.21595/JVE.2019.20781. [6]Mengyuan H, Qiaolin D, Shutao Z, et al. Research of Circuit Breaker Intelligent Fault Diagnosis Method Based on Double Clustering. Ieice Electronics Express, 2017, 14(17):20170463-20170463. DOI:10.1587/elex.14.20170463. [7]Hong C, Xiaojuan C, Wei X, et al. Short-time fourier transform based analysis to characterization of series arc fault. IEEE, 2009. DOI:10.1109/PEITS.2009.5407038. [8]Zhang J, Kong X, Cheng L, et al. Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism. Maintenance & Reliability / Eksploatacja i Niezawodność, 2023, 25(1). DOI:10.17531/ein.2023.1.16. [9]Liu Y, Xu Z, Guo P, et al. Abnormal-Sound Diagnosis for Kaplan Hydroelectric Generating Units Based on Continuous Wavelet Transform and Transfer Learning. Sensors, 2024, 24 (23): 7441-7441. [10]Xin L, Fangze W, Hao L, et al. Fault location of transmission lines by wavelet packet decomposition based on SSSC and EMD. Electrical Engineering, 2024, 106(6):7853-7866. DOI:10.1007/s00202-024-02484-8. [11]Chang Y, Bao G, Cheng T Y Q. Improved VMD-KFCM algorithm for the fault diagnosis of rolling bearing vibration signals. IET signal processing, 2021, 15(4):238-250. DOI:10.1049/sil2.12026. [12]Wangying C, Mingyue Y, Peng W. Rotor–Stator Rub-Impact Fault and Position Identification of Aero-Engine Based on VMD-MF-Cepstrum-KNN. Tribology Transactions, 2023. DOI:10.1080/10402004.2022.2131665. [13]Hu X, Zhou J, Li J, et al. Peak Detection Algorithm for Mass Spectrometry Integrating Weighted Continuous Wavelet Transform with Particle Swarm Optimization-Based Otsu. Analytical Letters, 2024, 57(11): 16. DOI:10.1080/00032719.2023.2266207. [14]Sun Y, Pang S, Qiu Z, et al. Improving fluid identification in well logging using Continuous Wavelet Transform and Vision Transformers: An innovative approach. Physics of Fluids, 2024, 36(10):22. DOI:10.1063/5.0221990. [15]Junfei D, Xinyu L, Yiping G, et al. Integrated Gradient-Based Continuous Wavelet Transform for Bearing Fault Diagnosis. Sensors, 2022, 22 (22): 8760-8760. [16]Loh C H, Wu T C, Huang N E. Application of the Empirical Mode Decomposition-Hilbert Spectrum Method to Identify Near-Fault Ground-Motion Characteristics and Structural Responses. Bulletin of the Seismological Society of America, 2001. DOI:10.1785/ 0120000715. [17]Cheng J, Yang Y, Yang Y. A rotating machinery fault diagnosis method based on local mean decomposition. Digital Signal Processing, 2012, 22(2):356-366. DOI:10.1016/j.dsp.2011.09.008. [18]Tabrizi A, Garibaldi L, Fasana A, et al. Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine. Meccanica, 2015, 50(3):865-874. DOI:10.1007/s11012-014-9968-z. [19]Ni J, Zhang C, Yang S X. An Adaptive Approach Based on KPCA and SVM for Real-Time Fault Diagnosis of HVCBs. IEEE Transactions on Power Delivery, 2011, 26(3):1960-1971. DOI:10.1109/TPWRD.2011.2136441. [20]Hou L, Huang Q. A smart WSNs node with sensor computing and unsupervised One-Class SVM classifier for machine fault detection. Measurement, 2025, 242 (PB): 115843-115843. [21]Guo R, Wang W, Zhang Y, et al. SVM Multiclass Fault Diagnosis Based on Scaled Convex Shell and Parameter Optimization//International conference on the Efficiency and Performance Engineering Network, International Conference on Maintenance Engineering, International Workshop on Defence Applications of Multi-Agent Systems. Springer, Cham, 2024. DOI:10.1007/978-3-031-49413-0_64. [22]Tu Y, Xiang Y. Transformer fault diagnosis based on improved dung beetle algorithm optimizing SVM. Proceedings of SPIE, 2024, 12981(000):6. DOI:10.1117/12.3014961. [23]Song X, Wei W, Zhou J, et al. Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis. Sensors (14248220), 2023, 23(11). DOI:10.3390/s23115137. [24]Zhao L, Yu W, Yan R. Rolling Bearing Fault Diagnosis Based on CEEMD and Time Series Modeling. Mathematical Problems in Engineering, 2014, (2014-7-7), 2014, 2014:1-13. DOI:10.1155/2014/101867. [25]Li M, Wang H, Tang G, et al. An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing. Advances in Mechanical Engineering, 2014, 2014:1-10. DOI:10.1155/2014/676205. [26]Lixiang Y, Qinghe H, Shuang Z. Research on fault feature extraction method of rolling bearing based on improved wavelet threshold and CEEMD. Journal of Physics: Conference Series, 2020, 1449 012003-012003. [27]Xihui C, Gang C, Hongyu L, et al. Research of Planetary Gear Fault Diagnosis Based on Multi-Scale Fractal Box Dimension of CEEMD and ELM. Strojniški vestnik - Journal of Mechanical Engineering, 2017, 63 (1): 45-55. [28]Jia Q Q, Shi L L, Wang N, et al. A Fusion Method for Ground Fault Line Detection in Compensated Power Networks Based on Evidence Theory and Information Entropy. Transactions of China Electrotechnical Society, 2012, 27(6):191-197. [29]Quansheng J, Yehu S, Hua L, et al. New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network. Sensors, 2018, 18(2):337. DOI:10.3390/s18020337. [30]Zheng N, Li Y, Shi W, et al. Sparse Bayesian based NARX modeling of cortical response: Introducing information entropy for enhancing the stability. Neurocomputing, 2025, 626 129569-129569. [31]Hrelja M, Klancnik S, Balic J, et al. Modelling of a Turning Process Using the Gravitational Search Algorithm. International Journal of Simulation Modelling, 2014, 13(1):30-41. DOI:10.2507/IJSIMM13(1)3.248. [32]Chatterjee A, Ghoshal S P, Mukherjee V. A maiden application of gravitational search algorithm with wavelet mutation for the solution of economic load dispatch problems. International Journal of Bio-Inspired Computation, 2012, volume 4(1):33-46(14). DOI:10.1504/IJBIC.2012.044934. [33]Rashedi E, Nezamabadi-Pour H. Improving the precision of CBIR systems by feature selection using binary gravitational search algorithm//Csi International Symposium on Artificial Intelligence & Signal Processing. IEEE, 2012. DOI:10.1109/AISP.2012.6313714. [34]Askari H, Zahiri S H. Decision function estimation using intelligent gravitational search algorithm. International Journal of Machine Learning & Cybernetics, 2012, 3(2):163-172. DOI:10.1007/s13042-011-0052-x. [35]Shuaib Y M, Kalavathi M S, Rajan C C A. Optimal Reconfiguration in Radial Distribution System Using Gravitational Search Algorithm. Electric Power Components and Systems, 2014, 42(5-8):703-715. DOI:10.1080/15325008.2014.890971. [36]Salahshoor K, Kordestani M, Khoshro M S. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers. Energy, 2010, 35(12):5472-5482. DOI:10.1016/j.energy.2010.06.001. [37]Xiao Y, Wang H, Zhang L, et al. Two methods of selecting Gaussian kernel parameters for One-Class SVM and their application to fault detection. Knowledge-Based Systems, 2014, 59(2):75-84. DOI:10.1016/j.knosys.2014.01.020. [38]Wei Y, Zhao J, Yang Z, et al. Fault detection method for flexible DC grid based on CEEMDAN multiscale entropy and GA-SVM. Electrical Engineering, 2024, 106(5):6171-6183. DOI:10.1007/s00202-024-02349-0. [39]Kazemi Z, Naseri F, Yazdi M F E. An EKF-SVM machine learning-based approach for fault detection and classification in three-phase power transformers. IET science, measurement & technology, 2021, 15(2):130-142. DOI:10.1049/smt2.12015.