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Power Quality Improvement of Power Electronics Systems by using Machine Learning

Author Affiliations

  • 1Department of Electrical Engineering, Bhilai Institute of Technology, Durg, CG, India
  • 2Department of Electrical Engineering, Bhilai Institute of Technology, Durg, CG, India

Res. J. Engineering Sci., Volume 13, Issue (1), Pages 24-27, January,26 (2024)


The power electronic components or devices are the main focus of this report. It gains more popularity because of its reduced size and smooth control on output voltage & current. It is very popular in areas of renewable energy sources (as converters & inverters) and industrial drives (as controlling devices). In both cases, the power electronic components and devices act as non-linear loads due to its switching process. This causes system to generate the harmonics or cause of power quality problem. This should be mitigated or eliminated by using different methods, recommended by some standards e.g. IEEE-519. The limits and guidelines are also given by them to help the customers and manufacturers. The improvement in power quality can be achieved by Machine learning algorithms. Machine learning is another very popular area, which uses the data of any system and predicts the system output after training the model (e.g. for classification of fault type and identification of exact location of faults, predicting the life of power electronics components, detection of voltage disturbances). Depending on different techniques of machine learning, different applications can be achieved.


  1. Chandra, A., Singh, B., Singh, B. N., & Al-Haddad, K. (2000)., An improved control algorithm of shunt active filter for voltage regulation, harmonic elimination, power-factor correction, and balancing of nonlinear loads., IEEE transactions on Power electronics, 15(3), 495-507.
  2. Srivastav, A., Chauhan, A., & Tripathi, A. (2020)., Mitigation of harmonics in voltage and current using UPQC., In 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC). 456-460. IEEE.
  3. Singh, B. N., Singh, B., Chandra, A., Rastgoufard, P., & Al-Haddad, K. (2007)., An improved control algorithm for active filters., IEEE Transactions on Power Delivery, 22(2), 1009-1020.
  4. Singh, B., Singh, B. N., Chandra, A., Al-Haddad, K., Pandey, A., & Kothari, D. P. (2004)., A review of three-phase improved power quality AC-DC converters., IEEE Transactions on industrial electronics, 51(3), 641-660.
  5. Singh, B., Al-Haddad, K., & Chandra, A. (1999)., A review of active filters for power quality improvement., IEEE transactions on industrial electronics, 46(5), 960-971.
  6. Singh, B., Al-Haddad, K., & Chandra, A. (1999)., A review of active filters for power quality improvement., IEEE transactions on industrial electronics, 46(5), 960-971.
  7. Goldemberg, C., Pellini, E. L., Kaiser, W., & Komatsu, W. (2009)., A Python based power electronics E-learning tool., In 2009 Brazilian Power Electronics Conference, 1088-1092. IEEE.
  8. Bedi, G., Venayagamoorthy, G. K., Singh, R., Brooks, R. R., & Wang, K. C. (2018)., Review of Internet of Things (IoT) in electric power and energy systems., IEEE Internet of Things Journal, 5(2), 847-870.
  9. Akagi, H., Kanazawa, Y., & Nabae, A. (1984)., Instantaneous reactive power compensators comprising switching devices without energy storage components., IEEE Transactions on industry applications, (3), 625-630.
  10. Samanta, I. S., Rout, P. K., Swain, K., Cherukuri, M., & Mishra, S. (2022)., Power quality events recognition using enhanced empirical mode decomposition and optimized extreme learning machine., Computers and Electrical Engineering, 100, 107926.
  11. Lucas, K. E., Pagano, D. J., Vaca-Benavides, D. A., Garcia-Arcos, R., Rocha, E. M., Medeiros, R. L., & Rios, S. J. (2020)., Robust control of interconnected power electronic converters to enhance performance in DC distribution systems: A case of study., IEEE Transactions on Power Electronics, 36(4), 4851-4863.
  12. Vazquez, J. R., & Salmeron, P. (2003)., Active power filter control using neural network technologies., IEE Proceedings-Electric Power Applications, 150(2), 139-145.
  13. Li, Q., Jiang, D., Zhang, Y., & Liu, Z. (2020)., The impact of VSFPWM on DQ current control and a compensation method., IEEE Transactions on Power Electronics, 36(3), 3563-3572.
  14. Guillod, T., Papamanolis, P., & Kolar, J. W. (2020)., Artificial neural network (ANN) based fast and accurate inductor modeling and design., IEEE Open Journal of Power Electronics, 1, 284-299.
  15. Gautam, M., Raviteja, S., & Mahalakshmi, R. (2019)., Energy management in electrical power system employing machine learning., In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), 915-920. IEEE.
  16. Goswami, T., & Roy, U. B. (2019)., Predictive model for classification of power system faults using machine learning., In TENCON 2019-2019 IEEE Region 10 Conference (TENCON), 1881-1885. IEEE.
  17. Jahns, T. M., & Dai, H. (2017)., The past, present, and future of power electronics integration technology in motor drives., CPSS Transactions on Power Electronics and Applications, 2(3), 197-216.
  18. Kennel, R., & Linder, A. (2000)., Predictive control of inverter supplied electrical drives., In 2000 IEEE 31st Annual Power Electronics Specialists Conference. Conference Proceedings (Cat. No. 00CH37018). 2, 761-766. IEEE.
  19. Krishnamoorthy, H. S., & Aayer, T. N. (2019)., Machine learning based modeling of power electronic converters., In 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 666-672. IEEE.
  20. Mazzanti, G., Diban, B., Chiodo, E., De Falco, P., & Noia, L. P. D. (2020)., Forecasting the reliability of components subjected to harmonics generated by power electronic converters., Electronics, 9(8), 1266.
  21. Peyghami, S., Blaabjerg, F., & Palensky, P. (2020)., Incorporating power electronic converters reliability into modern power system reliability analysis., IEEE Journal of Emerging and Selected Topics in Power Electronics, 9(2), 1668-1681.
  22. Quinn, C., & Dalal, D. (2017)., The 2017" Power Technology Roadmap": Empowering the Electronics Industry [PSMA Corner]., IEEE Power Electronics Magazine, 4(2), 20-23.
  23. Takamiya, M., Miyazaki, K., Obara, H., Sai, T., Wada, K., & Sakurai, T. (2017)., Power electronics 2.0: IoT-connected and Al-controlled power electronics operating optimally for each user., 29th International Symposium on Power Semiconductor Devices and IC
  24. Turovic, R., Stanisavljevic, A., Dragan, D., & Katic, V. (2019)., Machine learning for application in distribution grids for power quality applications., 20th International Symposium on Power Electronics (Ee). 1-6. IEEE.
  25. Wang, X., & Blaabjerg, F. (2018)., Harmonic stability in power electronic-based power systems: Concept, modeling, and analysis., IEEE Transactions on Smart Grid, 10(3), 2858-2870.
  26. Holmberg, K., Andersson, P., & Erdemir, A. (2012)., Global energy consumption due to friction in passenger cars., Tribology international, 47, 221-234.
  27. Holmberg, K., Siilasto, R., Laitinen, T., Andersson, P., & Jäsberg, A. (2013)., Global energy consumption due to friction in paper machines., Tribology International, 62, 58-77.
  28. Rajeev Kumar Chauhan and J.P. Pandey (2014)., Mitigation of Power Quality Problems Using FACTS Devices: A Review., International Journal of Electronic and Electrical Engineering, 7(3), 255-262
  29. Aminifar, F., Teimourzadeh, S., Shahsavari, A., Savaghebi, M., & Golsorkhi, M. S. (2021)., Machine learning for protection of distribution networks and power electronics-interfaced systems., The Electricity Journal, 34(1), 106886.
  30. Yang, H., Liu, X., Zhang, D., Chen, T., Li, C., & Huang, W. (2021)., Machine learning for power system protection and control., The Electricity Journal, 34(1), 106881.
  31. Farhoumandi, M., Zhou, Q., & Shahidehpour, M. (2021). A review of machine learning applications in IoT-integrated modern power systems. The Electricity Journal, 34(1), 106879., undefined, undefined
  32. Fuchs, E. F., & Masoum, M. A. (2011)., Power quality in power systems and electrical machines., Academic press.