Comparison of PI and Artificial Neural Network Controller Based DVR for Compensation of Voltage Sag

Authors

  • Shubham Yadav  Electrical Engineering, RSRRCET Bhilai, Chhattisgarh, India
  • Manish Chandrakar  Electrical Engineering, RSRRCET Bhilai, Chhattisgarh, India

Keywords:

Power quality, Dynamic voltage restorer, Proportional Integral, Artificial Neural Network(ANN)

Abstract

With the advent of power electronic devices, the issue of power quality has become a primary concern in today's power systems. The performance of sensitive loads is greatly affected by the inadequate power quality. The Dynamic Voltage Restorer (DVR) is a custom power device that serves as an efficient solution for safeguarding sensitive loads against voltage disturbances within power distribution systems. The efficiency of the Dynamic Voltage Restorer (DVR) depends on the effectiveness of the control technique employed to manage the switching of the inverters. The Proportional-Integral (PI) control is a commonly used technique for managing the switching of inverters in order to achieve efficient operation of the Dynamic Voltage Restorer (DVR). The application of the linear Proportional-Integral (PI) control technique to control a non-linear Dynamic Voltage Restorer (DVR) has limitations in terms of power quality restoration capabilities and leads to the generation of significant harmonics. In this paper a reliable and effective adaptive neural network based controller is used for enhancing restoration operation and harmonic suppression capabilities of DVR. Furthermore, the comparative analysis between PI controller and ANN based controller is also presented. The results of the analysis demonstrate that the ANN based DVR exhibits superior performance compared to the PI-based controller.

References

  1. Shuhong, Kong & Zhongdong, Yin & Renzhong, Shan & Weidong, Shang. (2009). A Survey on the Principle and Control of Dynamic Voltage Restorer. 57 - 60. 10.1109/ICEET.2009.250.
  2. R. Omar and N. A. Rahim, "New control technique applied in dynamic voltage restorer for voltage sag mitigation," 2009 4th IEEE Conference on Industrial Electronics and Applications, Xi'an, China, 2009, pp. 848-852, doi: 10.1109/ICIEA.2009.5138322.
  3. Kantaria R. A. ; Joshi S.K.; Siddhapura K. R., "A novel technique for mitigation of voltage sag/swell by Dynamic Voltage Restorer (DVR)," Electro/Information Technology (EIT), 2010 IEEE International Conference on , vol., no., pp.1,4, 20- 22 May 2010.
  4. Elango, S.; Chandra Sekaran, E., "Mitigation of Voltage Sag by Using Distribution Static Compensator (D-STATCOM)," Process Automation, Control and Computing (PACC), 2011 International Conference on, vol., no., pp.1,6, 20- 22 July 2011.
  5. M. Faisal, M. S. Alam, M. I. M. Arafat, M. M. Rahman and S. M. G. Mostafa, "PI controller and park's transformation based control of dynamic voltage restorer for voltage sag minimization," 2014 9th International Forum on Strategic Technology (IFOST), Cox's Bazar, Bangladesh, 2014, pp. 276-279, doi: 10.1109/IFOST.2014.6991121.
  6. P. V. Dhote, B. T. Deshmukh and B. E. Kushare, "Generation of power quality disturbances using MATLAB-Simulink," 2015 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), Melmaruvathur, India, 2015, pp. 0301-0305, doi: 10.1109/ICCPEIC.2015.7259479.
  7. M. H. Bollen, R. Das, S. Djokic, P. Ciufo, J. Meyer, S. K. Rönnberg, et al., "Power quality concerns in implementing 5571 smart distribution-grid applications," IEEE Transactions on Smart Grid, vol. 8, pp. 391-399, 2017.
  8. C. Subramani, A. A. Jimoh, S. H. Kiran and S. S. Dash, "Artificial neural network based voltage stability analysis in power system," 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, India, 2016, pp. 1-4, doi: 10.1109/ICCPCT.2016.7530255.
  9. M. S. Haque Sunny, E. Hossain, M. Ahmed and F. Un-Noor, "Artificial Neural Network Based Dynamic Voltage Restorer for Improvement of Power Quality," 2018 IEEE Energy Conversion Congress and Exposition (ECCE), Portland, OR, USA, 2018, pp. 5565-5572, doi: 10.1109/ECCE.2018.8558470.
  10. Tekwani, P. N. & Chandwani, Ashwin & Sankar, Sagar & Gandhi, Neel & Chauhan, Siddharthsingh. (2020). Artificial neural network-based power quality compensator. International Journal of Power Electronics. 11. 256. 10.1504/IJPELEC.2020.105151.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2023-06-16

Issue

Volume 10, Issue 3, May-June-2023

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shubham Yadav, Manish Chandrakar "Comparison of PI and Artificial Neural Network Controller Based DVR for Compensation of Voltage Sag" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 3, pp.471-476, May-June-2023.