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Identification of Catenary Performance Degradation Based on Gath Geva Clustering and Improved Support Vector Date Description

  • Research Article-Electrical Engineering
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Abstract

In view of the low utilization of catenary detection data, the imperfect research on catenary performance degradation caused by the joint action of multiple parameters, and most of the data are unsupervised learning data, which is not conducive to research and analysis. A catenary performance degradation identification method based on the improved support vector data description model (ISVDD) optimized by Gath Geva (GG) clustering and particle swarm optimization (PSO) algorithm is proposed. Firstly, select the semi supervised learning data, cluster the unlabeled data with t-distributed stochastic neighbor embedding dimension reduction method and GG clustering method, reduce the original data to two-dimensional space, and get the best number of clusters. Then, a PSO optimized ISVDD catenary performance degradation model is constructed using labeled data, which contains as much normal data and as little abnormal data as possible, and the radius of the established hypersphere model is as small as possible. Finally, the distance between the clustered sample data and the spherical center of the hypersphere is calculated, and the distance and radius are compared to determine the performance degradation degree of catenary. The simulation results show that the PSO-ISVDD proposed in this paper can judge the performance degradation of catenary more accurately than SVDD and ISVDD, and the accuracy can be as high as 99.33%, which provides a powerful reference for the identification of catenary performance degradation.

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References

  1. Liu, Z.-G.; Song, Y.; Han, Y.; Wang, H.-R.; Zhang, J.; Han, Z.-W.: Research progress of high speed railway catenary. J. Southwest Jiaotong Univ. 51(03), 495–518 (2016)

    Google Scholar 

  2. Feng, D.; Yu, Q.; Sun, X.; Zhu, H.; Lin, S.; Liang, J.: Risk assessment for electrified railway catenary system under comprehensive influence of geographical and meteorological factors. IEEE Trans. Transp. Electr. 7(4), 3137–3148 (2021)

    Article  Google Scholar 

  3. Song, Y.; Li, L.: Robust adaptive contact force control of pantograph-catenary system: an accelerated output feedback approach. IEEE Trans. Industr. Electron. 68(8), 7391–7399 (2021)

    Article  Google Scholar 

  4. Qin, X.-Y.; Shi, B.: Performance degradation analysis of high-speed railway catenary based on continuous hidden Markov model. Electrified Railway 29(S1), 51–56 (2018)

    Google Scholar 

  5. Shi, B.; Gao, S.-B.: Performance degradation analysis of high speed railway catenary based on HHT method. Electrified Railway 28(04), 26–29 (2017)

    Google Scholar 

  6. Zhai, W.-R.; Li, X.-G.; Wang, J.-G.; Ding, W.-K.: Research on performance degradation evaluation method and application of shearer. Ind. Min. Autom. 46(12), 57-63+100 (2020)

    Google Scholar 

  7. Ziwen, C.; Feng, Z.; Ying, W.; Xiaoqiang, C.: High speed railway catenary health status assessment based on improved multi class evidence body method. Measur. Control Technol. 39(09), 128–132 (2020)

    Google Scholar 

  8. Wang, B.; Hu, X.; Li, H.-R.; Sun, D.-J.: Bearing performance degradation state identification based on basic scale entropy and GG fuzzy clustering. Vib. and Impact 38(05), 190-197+221 (2019)

    Google Scholar 

  9. Zhang, L.; Huang, W.-Y.; Xiong, G.-L.; Zhou, J.-M.; Zhou, J.-H.: Performance degradation evaluation of rolling bearing based on tespar and GMM. J. Instrum. 35(08), 1772–1779 (2014)

    Google Scholar 

  10. Gao, S.-J.: Fuzzy SVDD bearing performance degradation evaluation based on GA stochastic resonance. Modular Mach. Tool Autom. Mach. Technol. 08, 53–58 (2019)

    Google Scholar 

  11. Xuyong, H.: Performance degradation evaluation of Railway Catenary Based on IK means and CHMM. Locom. Electric Drive 02, 140–145 (2021)

    Google Scholar 

  12. Sun, M.-W.; Yang, H.-Z.: Clustering multi model soft sensor modeling based on support vector data description. Control Eng. 25(07), 1184–1189 (2018)

    Google Scholar 

  13. Saeed, I.M.; Mazari, S.A.; Alaba, P., et al.: A review of gas chromatographic techniques for identification of aqueous amine degradation products in carbonated environments. Environ. Sci. Pollut. Res. 28, 6324–6348 (2021)

    Article  Google Scholar 

  14. Karakose, E.; Gencoglu, M.T.; Karakose, M., et al.: A new arc detection method based on fuzzy logic using S-transform for pantograph–catenary systems. J. Intell. Manuf. 29, 839–856 (2018)

    Article  Google Scholar 

  15. Mao, X.-D.; Wang, Q.-X.; Dong, W.-G.; Liang, J.-P.; Zhu, K.: Fault diagnosis of phase shifted full bridge converter based on wavelet packet neural network and data dimension reduction. Acta Sinica Sinica 04, 68–75 (2014)

    Google Scholar 

  16. Burnaev, E.V.; Bernstein, A.V.: Functional dimension reduction in predictive modeling. J. Commun. Technol. Electron. 66, 745–753 (2021)

    Article  Google Scholar 

  17. Yang, F.; Feng, X.; Ruan, L.; Chen, J.-W.; Xia, R.; Chen, Y.-L.; Jin, Z.-H.: Correlation study of water branch and ultra-low frequency dielectric loss based on Pearson correlation coefficient method. High Volt. Electr. Appl. 50(06), 21-25+31 (2014)

    Google Scholar 

  18. Toubiana, D.; Maruenda, H.: Guidelines for correlation coefficient threshold settings in metabolite correlation networks exemplified on a potato association panel. BMC Bioinformatics 22, 116 (2021)

    Article  Google Scholar 

  19. Xu, S.; Hua, X.-P.; Xu, J.; Xu, X.-F.; Gao, J.; An, J.: A clustering ensemble method based on t-distribution random nearest neighbor embedding. Acta Electron. Sin. 40(06), 1316–1322 (2018)

    Google Scholar 

  20. Jiang, W.; Zhou, J.-Z.; Liu, H.; Shan, Y.-H.: A multi-step progressive fault diagnosis method for rolling element bearing based on energy entropy theory and hybrid ensemble auto-encoder. ISA Trans. 87, 235–250 (2019)

    Article  Google Scholar 

  21. Liu, Z.-G.; Nie, R.-X.; Liu, Y.: Effects of total soy saponins on free radicals in the quadriceps femoris, serum testosterone, LDH, and BUN of exhausted rats. J. Sport Health Sci. 6(03), 359–364 (2017)

    Article  Google Scholar 

  22. Li, H.-Z.; Wang, X.; Guo, Y.: Feature extraction and classification of power grid data based on KL transform and KL divergence. Electr. Measure. Instrum. 56(06), 87–92 (2019)

    Google Scholar 

  23. Fan, H.; Li, X.-W.; Su, H.-B.; Chen, L.; Shi, Z.-Q.: Research on fault diagnosis method of circuit breaker based on principal component analysis support vector machine optimization model. High Volt. Apparat. 56(06), 143–151 (2020)

    Google Scholar 

  24. Kamrani, H.; Asli, A.Z.; Markopoulos, P.P.; Langberg, M.; Pados, D.A.; Karystinos, G.N.: Reduced-Rank L1-norm principal-component analysis with performance guarantees. IEEE Trans. Signal Process. 69, 240–255 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  25. Wang, B.; Wang, W.; Hu, X.; Sun, D.-J.: Degradation state recognition method based on GG fuzzy clustering. J. Instrum. 39(03), 21–28 (2018)

    Google Scholar 

  26. Wang, T.; Yang, Y.; Gu, X.-P.; Zhang, X.-C.; Zhang, W.-C.: Coherent cluster recognition based on wavelet fuzzy entropy GG clustering. Power Autom. Equip. 38(07), 140–147 (2018)

    Google Scholar 

  27. Wu, M.; Xu, C.-H.; Wang, C.-S.: Prediction of comprehensive permeability state in lead zinc sintering process based on fuzzy classification variable coefficient. J. East China Univ. Sci. Technol. 07, 825-828+871 (2006)

    Google Scholar 

  28. Alam, S.; Sonbhadra, S.K.; Agarwal, S.: Sample reduction using farthest boundary point estimation (FBPE) for support vector data description (SVDD). Pattern Recognit. Lett. 131, 268–276 (2020)

    Article  Google Scholar 

  29. Li, X.; Zhang, Y.; Han, Y., et al.: Preload state detection for precision spindle bearings based on multi-level classification. J. Mech. Sci. Technol. 34, 4393–4403 (2020)

    Article  Google Scholar 

  30. Tang, S.; Huang, X.; Li, Q., et al.: Optimal sizing and energy management of hybrid energy storage system for high-speed railway traction substation. J. Electr. Eng. Technol. 16, 1743–1754 (2021)

    Article  Google Scholar 

  31. Li, G.; Yinghong, Xu.; Qin, Z.: Fenchel-Lagrange duality for DC infinite programs with inequality constraints. J. Comput. Appl. Math. 391, 0377–0427 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  32. Zhang, J.; Wang, Y.; Li, Q.; Wang, B.: Using a dynamically selective support vector data description model to discover novelties in the control system of electric arc furnace. Measur. Control 53(7–8), 1049–1058 (2020)

    Article  Google Scholar 

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Funding

The funding was provided by National Natural Science Foundation of China (61572416), Hunan Province Natural Science Zhuzhou United Foundation (2020JJ6009), Postgraduate Scientific Research Innovation Project of Hunan Province (QL20210153), Key Laboratory Open Project Fund of Disaster Prevention and Mitigation for Power Grid Transmission and Transformation Equipment.

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Authors and Affiliations

  1. College of Automation and Electronic Engineering, Xiangtan University& Hunan Engineering Research Center of Multi-energy Cooperative Control Technology, Xiangtan, China

    Lingzhi Yi, Tao Sun, Jian Zhao, Xiu Xu, Ganlin Jiang & Jiangyong Liu

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  1. Lingzhi Yi
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  2. Tao Sun
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  3. Jian Zhao
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  4. Xiu Xu
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  5. Ganlin Jiang
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  6. Jiangyong Liu
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Correspondence to Tao Sun.

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Yi, L., Sun, T., Zhao, J. et al. Identification of Catenary Performance Degradation Based on Gath Geva Clustering and Improved Support Vector Date Description. Arab J Sci Eng 47, 13765–13780 (2022). https://doi.org/10.1007/s13369-021-06393-x

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  • DOI: https://doi.org/10.1007/s13369-021-06393-x

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