Adaptive Neuro-Fuzzy Inference System-Based Control Strategy for Dynamic Voltage Restorer (DVR) for Both Voltage Sag/Swell and Unbalance Compensation

  • Ehsan Akbari Mazandaran University of Science and Technology
Keywords: Power Quality, Voltage Sag /Swell, DVR, Sugeno fuzzy controller, ANFIS.


PI controller is very common in the control of DVRs. However, one disadvantage of this conventional controller is its inability to still working well under a wider range of operating conditions. So, as a solution fuzzy controller is proposed in literature. But, the main problem with the conventional fuzzy controllers is that the parameters associated with the membership functions and the rules depend broadly on the intuition of the experts. To overcome this problem, Adaptive Neuro-Fuzzy Inference System (ANFIS) based controller design is proposed. The resulted controller is composed of Sugeno fuzzy controller with two inputs and one output. According to the error and error rate of the control system and the output data, ANFIS generates the appropriate fuzzy controller. The simulation results have proved that the proposed design method gives reliable powerful fuzzy controller with a minimum number of membership functions. The Dynamic Voltage Restorer (DVR) is fast, flexible and efficient solution to voltage problem. The DVR is designed for protecting the whole plant with loads in the range of some MVA. The DVR can restore the load voltage within few milliseconds. The simulation results are obtained through MATLAB/SIMULINK software.


[1] Lim, P.K, Dorr, D.S, "Understanding and resolving voltage sag related problems for sensitive industrial customers", Power Engineering Society Winter Meeting, 2000. IEEE, Volume 4, Jan 2000, Page(s):2886- 2890.
[2] IEEE Std. 1159 – 1995, “Recommended Practice for Monitoring Electric Power Quality”.
[3] K.Youssef, “Industrial power quality problems Electricity Distribution,” IEE Conf. Pub1 No. 482,Vol: 2, 18-21 June 2001 Pages: 5 pp. vo1.2
[4] B. H. Li, S. S. Choi, and D. M. Vilathgamuwa, "Design considerations on the line-side filter used in the dynamic
voltage restorer," IEE Proceedings - Generation, Transmission, and Distribution, vol. 148, pp. 1-7, Jan. 2001.
[5] P. Mitra, S. Maulik, S. P. Chowdhury and S. Chowdhury, "ANFIS Based Automatic Voltage Regulator with Hybrid Learning Algorithm" International Journal of Innovations in Energy Systems and Power, Vol. 3 no. 2, October 2008.
[6] E. Akbari, „A Survey on Control Strategies of Dynamic Voltage Restorer‟ 13th International Conference on Harmonics and Quality of Power (ICHQP), Sept. 28 2008-Oct. 1 2008, pp: 1-5, Wollongong, NSW.
[7] Mahesh, S.S. Mishra, M.K. Kumar, B.K. Jayashankar, V.‟ Rating and Design Issues of DVR Injection Transformer‟ Applied Power Electronics Conference and Exposition (APEC) 2008. Twenty-Third Annual IEEE, 24-28 Feb. 2008, pp: 449- 455, Austin, TX
[8] Hyosung Kim, Jang-Hwan Kim, Seung-Ki Sul, "A design consideration of output filters for dynamic voltage restorers," Power Electronics Specialists Conference, 2004. PESC 04.2004 IEEE 35th Annual,
Volume 6, 20-25 June 2004 Page(s):4268 - 4272 Vo1.6
[9] M. H. J. Bollen, “Understanding Power Quality Problems,” New York: IEEE Press, 2000
[10] Bingsen Wang, Giri Venkataramanan, „Dynamic Voltage Restorer Utilizing a Matrix Converter and Flywheel Energy Storage‟, IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 45, NO. 1, JANUARY/FEBRUARY 2009
[11] J. S. R. Jang, "ANFIS: Adaptive-Networkbased Fuzzy Inference Systems", IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 03, pp. 665-685,
[12] Srinivasan Alavandar and M. J. Nigam, "Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator" Journal of Engineering Science and Technology Review 1 (2008) 106- 111.
How to Cite
Akbari, E. (2021). Adaptive Neuro-Fuzzy Inference System-Based Control Strategy for Dynamic Voltage Restorer (DVR) for Both Voltage Sag/Swell and Unbalance Compensation. Majlesi Journal of Energy Management, 9(4). Retrieved from