RISK DETECTION OF OPERATIONAL FAULTS IN DISTRIBUTION GRID EQUIPMENT BASED ON SUPPORT VECTOR MACHINES AND RISK CHARACTERIZATION

Ning Gao, Chao Wu, Ling Liu, Fei Yang, Na Liu, and Chen Shen

View Full Paper

References

  1. [1] Y. Meysam, F. Z. Seyed, M. Nader, and B. Frede, “ADistributed High-Impedance Fault Detection and ProtectionScheme in DC Microgrids,” IEEE Transactions on Power De-livery, vol. 39, no. 1, pp. 141–154, 2024.
  2. [2] M. G. Jose, N. Gustavo, M. Kumar, and P. Carlos, “GroundFault Detection Method for Variable Speed Drives,” IEEETransactions on Industry Applications, vol. 57, no. 3, pp. 2547–2558, 2021.
  3. [3] P. P. Hwa, K. Mina, J. Jee-Hoon, and C. Suyong, “Series DCArc Fault Detection Method for PV Systems Employing Dif-ferential Power Processing Structure,” IEEE Transactions onPower Electronics, vol. 36, no. 9, pp. 9787–9795, 2021.
  4. [4] M. Kumar and J. Kumar, “Islanding event detection techniquebased on change in apparent power in microgrid environment,”Electrical engineering, vol. 105, no. 3, pp. 1447–1463, 2023.
  5. [5] Y. B. Hassan, M. Orabi, and M. A. Gaafar, “Failures causesanalysis of grid-tie photovoltaic inverters based on faults signa-tures analysis (FCA-B-FSA),” Solar Energy, vol. 262, no. Sep.,pp. 1.1–1.23, 2023.
  6. [6] P. Pietrzak, M. Wolkiewicz, and T. Orlowska-Kowalska,“PMSM Stator Winding Fault Detection and ClassificationBased on Bispectrum Analysis and Convolutional Neural Net-work,” IEEE Transactions on Industrial Electronics, vol. 70,no. 5, pp. 5192–5202, 2023.
  7. [7] S. Ghashghaei and M. Akhbari, “Fault detection and classifica-tion of an HVDC transmission line using a heterogenous multi-machine learning algorithm,” IET generation, transmission &distribution, vol. 15, no. 16, pp. 2319–2332, 2021.
  8. [8] Y. Xiu, L. Vu, and L. Inhwan, “Unknown Input Observer-BasedSeries DC Arc Fault Detection in DC Microgrids,” IEEE Trans-actions on Power Electronics, vol. 37, no. 4, pp. 4708–4718,2022.
  9. [9] M. Zhao and M. Barati, “A Real-Time Fault Localization inPower Distribution Grid for Wildfire Detection Through DeepConvolutional Neural Networks,” IEEE Transactions on Indus-try Applications, vol. 57, no. 4, pp. 4316–4326, 2021.
  10. [10] Y. ZHANG, X. SHI, H. ZHANG, et al., “Review on deep learn-ing applications in frequency analysis and control of modernpower system,” International Journal of Electrical Power & En-ergy Systems, 2022. 107744. DOI:10.1016/j.ijepes.2021.107744.
  11. [11] Z. LIU, G. WU, W. HE, et al., “Key target and defect detectionof high-voltage power transmission lines with deep learning,”International Journal of Electrical Power & Energy Systems,2022. 108277. DOI:10.1016/j.ijepes.2022.108277.
  12. [12] D. A. Mansour, M. Numair, A. S. Zalhaf, R. Ramadan, M. M. F.Darwish, Q. Huang, et al., “Applications of IoT and DigitalTwin in Electrical Power Systems: A Comprehensive Survey,”IET Generation, Transmission & Distribution, vol. 17, no. 20,pp. 4457–4479, 2023.
  13. [13] A. S. Desai, N. Navaneeth, S. Chakraborty, and S. Adhikari,“Enhanced Multi-Fidelity Modeling for Digital Twin and Un-certainty Quantification,” Probabilistic Engineering Mechanics,vol. 74, pp. 1–16, 2023.
  14. [14] S. Li, P. Zhang, D. Yue, and Q. Wang, “Fault Prediction ofWind Turbine Based on Support Vector Machine,” ComputerSimulation, vol. 39, no. 5, pp. 84–88, 180, 2022.
  15. [15] A. Jimenez-Cordero and S. Maldonado, “Automatic FeatureScaling and Selection for Support Vector Machine Classifica-tion with Functional Data,” Applied Intelligence, vol. 51, no. 1,pp. 161–184, 2021.11
  16. [16] S. Afrasiabi, M. Mohammadi, M. Afrasiabi, and B. Parang,“Modulated Gabor Filter Based Deep Convolutional Networkfor Electrical Motor Bearing Fault Classification and Diagno-sis,” IET Science, Measurement & Technology, vol. 15, no. 2,pp. 154–162, 2021.
  17. [17] P. Bernhard, “The First Unified and Transient Modeling Plat-form to Build a Digital Twin of Blast Furnaces Based on theExtended Discrete Element Method,” Steel Research Interna-tional, vol. 95, no. 3, pp. 1–14, 2024.
  18. [18] M. Xia, H. Shao, D. Williams, S. Lu, L. Shu, and C. W. Silva,“Intelligent Fault Diagnosis of Machinery Using Digital Twin-Assisted Deep Transfer Learning,” Reliability Engineering &System Safety, vol. 215, p. 107938, 2021.
  19. [19] S. N. Lahiri, “Sufficient and Necessary Conditions for Consis-tency of Variable Selection in High Dimensional Lasso,” TheAnnals of Statistics, vol. 49, no. 2, pp. 820–844, 2021.
  20. [20] F. Motamedi, H. Perez-Sanchez, A. Mehridehnavi, A. Fas-sihi, and F. Ghasemi, “Accelerating Big Data Analysis throughLASSO-Random Forest Algorithm in QSAR Studies,” Bioinfor-matics, vol. 38, no. 2, pp. 469–475, 2022.

Important Links:

Go Back