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Venkata, P., Pandya, V., Sant, A.V.. (1403). Data Mining and SVM Based Fault Diagnostic Analysis in Modern Power System Using Time and Frequency Series Parameters Calculated From Full-Cycle Moving Window. سامانه مدیریت نشریات علمی, 12(3), 206-214. doi: 10.22098/joape.2023.10819.1789
P. Venkata; V. Pandya; A.V. Sant. "Data Mining and SVM Based Fault Diagnostic Analysis in Modern Power System Using Time and Frequency Series Parameters Calculated From Full-Cycle Moving Window". سامانه مدیریت نشریات علمی, 12, 3, 1403, 206-214. doi: 10.22098/joape.2023.10819.1789
Venkata, P., Pandya, V., Sant, A.V.. (1403). 'Data Mining and SVM Based Fault Diagnostic Analysis in Modern Power System Using Time and Frequency Series Parameters Calculated From Full-Cycle Moving Window', سامانه مدیریت نشریات علمی, 12(3), pp. 206-214. doi: 10.22098/joape.2023.10819.1789
Venkata, P., Pandya, V., Sant, A.V.. Data Mining and SVM Based Fault Diagnostic Analysis in Modern Power System Using Time and Frequency Series Parameters Calculated From Full-Cycle Moving Window. سامانه مدیریت نشریات علمی, 1403; 12(3): 206-214. doi: 10.22098/joape.2023.10819.1789

Data Mining and SVM Based Fault Diagnostic Analysis in Modern Power System Using Time and Frequency Series Parameters Calculated From Full-Cycle Moving Window

Journal of Operation and Automation in Power Engineering
مقاله 3، دوره 12، شماره 3، آبان 2024، صفحه 206-214    اصل مقاله (1.05 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.10819.1789
نویسندگان
P. Venkata* ؛ V. Pandya؛ A.V. Sant
Electrical Engineering Department, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India.
چکیده
This paper proposes a complete diagnostic analysis of faults in a typical modern power system's transmission line using the support vector machine (SVM) with time-series parameters and frequency series parameters as features. The training and testing data of the proposed method are collected by simulating all types of faults with all possible variations on a transmission line (TL) in the IEEE-9 bus system using the PSCAD/EMTDC software. While simulating one type of fault, fault resistances and fault inception angles are also varied to account for the various behaviours of the fault. The three-phase instantaneous currents and voltages on both sides of TL are recorded at 32 samples per cycle. A thirty-two sample moving window is used to compute time-series and frequency-series parameters applied as features to the SVM. Ten-fold cross-validation is used to evaluate the performance of the proposed algorithm with evaluation metrics such as accuracy, precision, recall and F1 score. Features generation, training and testing of the proposed method, and performance comparison are done using PYTHON software. The proposed method has achieved an average accuracy of 99.996%, even in the most contaminated environment of 30 dB noise. Compared with the performance of the other popular machine learning algorithms, the proposed method has achieved more accuracy. The performance of the proposed method is also tested with different noise levels, which account for the measurement errors of 30 dB, 35 dB and 40 dB.
کلیدواژه‌ها
Data Mining؛ Fault classification؛ FFT؛ Machine Learning؛ SVM؛ Transmission line
مراجع
  1. Allen Wood, F. Bruce Wollenberg, and B. Gerald Sheblé, “Power generation, operation, and control,” John Wiley & Sons, 2013.
  2. Atul Raturi, “Renewables 2019 global status report,” Tech. Rep, REN21 Secretariat, 2019.
  3. Chen, “Fault statistics and analysis of 220-kV and above transmission lines in a southern coastal provincial power grid of China,” IEEE Open Access J. Power Energy, Vol. 7, pp. 122–129, 2020.
  4. M. Arias Velásquez, “Performance improvement in long overhead lines associated to single-phase faults due to atmospheric discharges,” Eng. Fail. Anal., Vol. 105, pp. 347–372, 2019.
  5. Haes Alhelou, M E. Hamedani-Golshan, T. Cuthbert Njenda, and P. Siano, “A survey on power system blackout and cascading events: Research motivations and challenges,” Energies, Vol. 12, No. 4, pp. 682, 2019.
  6. US DOE, “Enabling modernization of the electric power system,” Quadrennial techno. review, Vol. 22, 2015.
  7. J M. Maza-Ortega, E. Acha, S. García, and A. GómezExpósito, “Overview of power electronics technology and applications in power generation transmission and distribution,” Mod. Power Syst. Clean Energy, Vol. 5, No. 4, pp. 499–514, 2017.
  8. I. Henderson, D. Novosel, and M L. Crow, “Electric power grid modernization trends, challenges, and opportunities,” IEEE Advancing Techno. Humanity, 2017.
  9. National Academies of Sciences Engineering Medicine et al., The power of change: Innovation for development and deployment of increasingly clean electric power technologies, National Academies Press, 2016.
  10. Naderi, M. Pourakbari-Kasmaei, and M. Lehtonen, “Transmission expansion planning integrated with wind farms: A review, comparative study, and a novel profound search approach,” International Int. J. Electr. Power Energy Syst. , Vol. 115, pp. 105460, 2020.
  11. P L. Joskow, “Transmission capacity expansion is needed to decarbonize the electricity sector efficiently,” Joule, Vol. 4, No. 1, pp. 1–3, 2020.
  12. M. Zainuddin, MS. Abd Rahman, MZA. Ab Kadir, NH. Nik Ali, A. Zaipatimah, Miszaina Osman, M. Mansor, A. Mohd Ariffin, M. Syahmi Abd Rahman, SFM Nor, et al, “Review of thermal stress and condition monitoring technologies for overhead transmission lines: Issues and challenges,” IEEE Access, Vol. 8, pp. 120053–120081, 2020.
  13. Chatterjee and Sudipta Debnath, “A new protection scheme for transmission lines utilizing positive sequence fault components,” Electr. Power Syst. Res., Vol. 190, pp. 106847, 2021.
  14. Xiao-Ran, ZHOU. En-Zhe, YE. Li, DU. Shuang-Yu, and YU. Zhan-Qing, “Monitoring of transmission line wildfires using satellite remote sensing,”3rd Annual Int. Conf. Electron. Electri. Eng. Inf. Sci. (EEEIS 2017), Atlantis Press, 2017.
  15. M .Anthony Sleva, Protective relay principles, CRC Press, 2018.
  16. K.Avvari, and V. DM. Kumar ‘A novel hybrid multiobjective evolutionary algorithm for optimal power flow in wind, PV, and PEV systems,” J. Oper. Autom. Power Eng., 2022.
  17. Bhalja and RP. Maheshwari, “Waveletbased fault classification scheme for a transmission line using a support vector machine,” Electric Power Compon. Sys., Vol. 36, No. 10, pp. 1017–1030, 2008.
  18. Godoy, A. Celaya, H.J. Altuve, N. Fischer, and A. Guzmán, “Tutorial on single-pole tripping and reclosing," Western Protective Relay Conf., pp. 1–21, 2012.
  19. A. Jiang, J.Z. Yang, Y-H. Lin, C-W. Liu, and J-C. Ma, “An adaptive pmu based fault detection/location technique for transmission lines.i.theory and algorithms,” IEEE Trans. Power Delivery, Vol. 15, No. 2, pp. 486–493, 2000.
  20. J-A. Jiang, C-S. Chen, and C-W. Liu, “A new protection scheme for fault detection, direction discrimination, classification, and location in transmission lines,” IEEE Trans. Power Delivery, Vol. 18, No. 1, pp. 34–42, 2003.
  21. Asuhaimi Mohd Zin, M.Saini, M. Wazir Mustafa, A. Rizal Sultan, and Rahimuddin, “New algorithm for detection and fault classification on parallel transmission line using dwt and bpnn based on clarke’s transformation,” Neurocomputing, Vol. 168, pp. 983–993, 2015.
  22. Dash and SR. Samantaray, “An accurate fault classification algorithm using a minimal radial basis function neural network,” Int. J. Eng. Intelligent Sys. Electri. Eng. Commun., 2004.
  23. RN Mahanty and PB Dutta Gupta, “Application of rbf neural network to fault classification and location in transmission lines”, IEE Proc. Gener. Transm. Distrib., Vol. 151, No. 2, pp. 201–212, 2004.
  24. Dalstein and B. Kulicke, “Neural network approach to fault classification for high speed protective relaying,” IEEE Trans. Power Delivery, Vol. 10, No. 2, pp. 1002–1011, 1995.
  25.  P.Venkata, V. Pandya, and A.V. Sant. “Data mining model based differential microgrid fault classification using SVM considering voltage and current distortions,” J. Oper. Autom. Power Eng., 2022.
  26. A. Baherifard, R. Kazemzadeh, A.S. Yazdankhah, and M. Marzband, “Improving the effect of electric vehicle charging on imbalance index in the unbalanced distribution network using demand response considering data mining techniques,” J. Oper. Autom. Power Eng., Vol. 11, No. 3, pp. 182–192, 2023.
  27. Bhasker, S. K., et al. "Differential protection of ISPST using Chebyshev neural network," Oper. Autom. Power Eng., Vol. 11, No. 2, pp. 123-129, 2023.
  28. Mahanty and PB. Dutta Gupta, “A fuzzy logic-based fault classification approach using current samples only,” Electr. Power Syst. Res., Vol. 77, No. 5–6, pp. 501–507, 2007.
  29. Xu, M-Y. Chow, and L.S. Taylor, “Power distribution fault cause identification with imbalanced data using the data miningbased fuzzy classification e-algorithm,” IEEE Trans. Power Syst., Vol. 22, No. 1, pp. 164–171, 2007.
  30. J-SR. Jang, “Anfis: adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man Cybern., Vol. 23, No. 3, pp. 665–685, 1993.
  31. Hassan, “Adaptive neuro fuzzy inference system (anfis) for fault classification in the transmission lines,” Online J. Electron. Electr. Eng. (OJEEE), Vol. 2, pp. 2551–2555, 2010.
  32. Wang and WWL. Keerthipala, “Fuzzyneuro approach to fault classification for transmission line protection,” IEEE Trans. Power Delivery, Vol. 13, No. 4, pp. 1093–1104, 1998.
  33. J-SR. J.and C-T. Sun, “Neuro-fuzzy modeling and control,” the IEEE, Vol. 83, No. 3, pp. 378–406, 1995.
  34. Biswapriya, and S. Debnath. “Cross correlation aided fuzzy based relaying scheme for fault classification in transmission lines,” Eng. Sci. Technol. Int. J., Vol. 23.3, pp. 534-543, 2020.
  35. Alok, Palash Kumar Kundu, and A. Das. “Application of principal component analysis for fault classification in transmission line with ratio-based method and probabilistic neural network: a comparative analysis,” J. Inst. Eng. India Ser. B, Vol. 101.4, pp. 321-333, 2020.
  36. Y. Qi, O. Fink, and G. Sansavini. “Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction,” IEEE Trans. Ind. Electron., Vol. 65.1, pp. 561-569, 2017.
  37. Chen, Kunjin, Jun Hu, and Jinliang He. “Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder,” IEEE Trans. Smart Grid, Vol. 9, No. 3, pp. 1748–1758, 2016.
  38. Jamehbozorg and S. M. Shahrtash, “A decision-tree-based method for fault classification in single-circuit transmission lines,” IEEE Trans. Power Delivery, Vol. 25, No. 4, pp. 2190–2196, 2010.
  39. SR Samantaray, PK Dash, and G Panda, “Distance relaying for transmission line using support vector machine and radial basis function neural network,” International J. Electr. Power Energy Syst., Vol. 29, No. 7, pp. 551–556, 2007.
  40. B. Parikh, B. Das, and R. Maheshwari, “Fault classification technique for series compensated transmission line using support vector machine,” International Int. J. Electr. Power Energy Syst., Vol. 32, No. 6, pp. 629–636, 2010.
  41. Manohar and E. Koley, “Svm based protection scheme for microgrid,”Int. Conf. Intell. Comput. Instrum. Control Technol. (ICICICT), IEEE, pp. 429–432, 2017.
  42. Boswell, “Introduction to support vector machines,” Dep. Computer Sci. Eng. Univ. California San Diego, 2002.
  43. Livani and C.Y. Evrenosoglu, “A fault classification and˘ localization method for three-terminal circuits using machine learning,” IEEE Trans. Power Delivery, Vol. 28, No. 4, pp. 2282–2290, 2013.
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