STEMM Institute Press
Science, Technology, Engineering, Management and Medicine
Transmission Line Fault Detection Based on CEEMD-AOA-SVM
DOI: https://doi.org/10.62517/jike.202504105
Author(s)
Zhaohui Liu*
Affiliation(s)
Inner Mongolia Power (Group) Co., Ltd, Hohhot Power Supply Branch, Hohhot, Inner Mongolia, China *Corresponding Author.
Abstract
To accurately extract the characteristics of transmission line short-circuit fault, a fault detection method based on comprehensive ensemble empirical mode decomposition (CEEMD), arithmetic optimization algorithm (AOA) and support vector machine (SVM) is proposed. Taking the three-phase voltage of the line after fault as the feature vector, CEEMD method is used to decompose it, and a series of modal components are obtained. Calculate the multi-scale sample entropy (MSE) of each mode and the MSE of the obtained component is composed of the fault feature set. Because the penalty parameters and kernel function of SVM have a direct impact on the classification accuracy in the classification process, the parameters are determined by arithmetic optimization algorithm (AOA), and the obtained fault feature set is used for fault detection using AOA-SVM model. The simulation results indicate that the fault detection rate of CEEMD-AOA-SVM is 98.28%, which is significantly higher than that of EMD-SVM and CEEMD-SVM.
Keywords
Transmission Line; Fault Detection; Complementary Ensemble Empirical Mode Decomposition; Arithmetic Optimization Algorithm; Support Vector Machine
References
[1] Shu H, Han Y, Huang R, et al. Fault Model and Travelling Wave Matching Based Single Terminal Fault Location Algorithm for T-Connection Transmission Line: A Yunnan Power Grid Study. Energies, 2020, 13(6):1506. DOI: 10.3390/en13061506. [2] Altaie A S, Abderrahim M, Alkhazraji A A. Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection. Sensors (14248220), 2024, 24(7). DOI: 10.3390/s24072124. [3] Yang X, Choi M S, Lee S J. Double-Circuit Transmission Lines Fault location Algorithm for Single Line-to-Ground Fault. Journal of Electrical Engineering and Technology, 2007, 2(4): 434-440. DOI:10.5370/JEET.2007.2.4.434. [4] Wang Y, Chen L, Yao M, et al. Evaluating weather influences on transmission line failure rate based on scarce fault records via a bi-layer clustering technique. IET Generation, Transmission & Distribution, 2019, 13(23): 5305-5312. DOI:10.1049/iet-gtd.2019.0551. [5] Rezaei D, Gholipour M, Parvaresh F. A single-ended traveling-wave-based fault location for a hybrid transmission line using detected arrival times and TW's polarity. Electric Power Systems Research, 2022. DOI:10.1016/j.epsr.2022.108058. [6] Gao C, Wang S. Flexible DC transmission line fault type identification scheme based on Pearson correlation coefficient. Journal of Physics: Conference Series, 2024, 2846 (1): 012002-012002. [7] Shuma, Adhikari, Nidul, et al. Fuzzy logic based on-line fault detection and classification in transmission line. [J]. SpringerPlus, 2016, 5(1):1002-1002. DOI:10.1186/s40064-016-2669-4. [8] Altaie A S, Abderrahim M, Alkhazraji A A. Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection. Sensors (14248220), 2024, 24(7). DOI: 10.3390/s24072124. [9] Liang Y, Ding J, Li H, et al. Transmission line frequency-domain fault location method based on the phasor-time space curve characteristics that considers decaying DC deviation. International Journal of Electrical Power & Energy Systems, 2022. DOI: 10.1016/j.ijepes.2022.108308. [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] Wang W, Liu W, Lin C, et al. Fault detection system of subway sliding plug door based on adaptive EMD method. Measurement Science & Technology, 2024(1): 35. [12] Xiong J, Qian W, Cen J, et al. A fault diagnosis method for building electrical systems based on the combination of variational modal decomposition and new mutual dimensionless. Scientific Reports, 2023, 13. DOI: 10.1038/s41598-022-27031-y. [13] Wang H, Wan P. Sensitive features extraction of early fault based on EEMD and WPT. Transactions of Beijing Institute of Technology, 2013, 33(9):945-950. [14] Damine Y, Bessous N, Megherbi A C, et al. Early Bearing Fault Detection Using EEMD and Three-Sigma Rule Denoising Method. Mechanika, 2023, 29(4). DOI: 10.5755/j02.mech.32770. [15] Yang J, Chen J, Hong R, et al. Multi-Scale Fault Frequency Extraction Method Based on EEMD for Slewing Bearing Fault Diagnosis. Springer International Publishing, 2015. DOI:10.1007/978-3-319-13707-0_40. [16] 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. [17] Sen-Lin L U, Long W. Application of CEEMD-FFT in Roller Bearing Fault Diagnosis. Journal of Zhengzhou University, 2015. [18] A J, Bhavani R, Arulini A S, et al. CNN-SVM Based Fault Detection, Classification and Location of Multi-terminal VSC-HVDC System. Journal of Electrical Engineering & Technology, 2023. DOI:10.1007/s42835-023-01391-5. [19] 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. [20] Liang N, Yuan Z, Kang J, et al. A multi-output fault diagnosis framework for hydraulic system using a CNN-SVM hierarchical learning strategy. Measurement Science & Technology, 2024(7):35. DOI: 10.1088/1361-6501/ad3f3b. [21] Hocine B. Feature extraction based on vibration signal decomposition for fault diagnosis of rolling bearings. The International Journal of Advanced Manufacturing Technology, 2024(1/2): 130. DOI: 10.1007/s00170-023-12710-5. [22] Chen J, Lin C, Yao B, et al. Intelligent fault diagnosis of rolling bearings with low-quality data: A feature significance and diversity learning method. Reliability Engineering and System Safety, 2023, 237. DOI: 10.1016/j.ress.2023.109343. [23] Ke Z, Di C, Bao X. Adaptive Suppression of Mode Mixing in CEEMD Based on Genetic Algorithm for Motor Bearing Fault Diagnosis. IEEE Transactions on Magnetics, 2021, PP(99): 1-1. DOI: 10.1109/TMAG.2021.3082138. [24] Zouache D, Abualigah L, Boumaza. A guided epsilon-dominance arithmetic optimization algorithm for effective multi-objective optimization in engineering design problems. Multimedia Tools & Applications, 2024, 83(11). DOI: 10.1007/s11042-023-16633-x. [25] Liu Y, Chen M, Yin R, et al. Improved Arithmetic Optimization Algorithm with Multi-Strategy Fusion Mechanism and Its Application in Engineering Design. Journal of Applied Mathematics and Physics, 2024, 12(6): 2212-2253. DOI: 10.4236/jamp.2024.126134. [26] Singh S, Mittal N, Singh H, et al. A feature level image fusion for Night-Vision context enhancement using Arithmetic optimization algorithm based image segmentation. Expert Systems with Application, 2022. DOI: 10.1016/j.eswa.2022.118272.
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