AIMS Energy, 2016, 4(2): 363-378. doi: 10.3934/energy.2016.2.363 Research article Topical Section

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SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid

1 Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, L1H 7K4 ON, Canada
2 Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, L1H 7K4 ON, Canada
3 Electrical Engineering Department, Canadian, International College, Cairo, Egypt

Received: , Accepted: , Published:

Topical Section: Smart Grids and Networks

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Maintaining voltage stability, within acceptable levels, for islanded Microgrids (MGs) is a challenge due to limited exchange power between generation and loads. This paper proposes an algorithm to enhance the dynamic performance of islanded MGs in presence of load disturbance using Static VAR Compensator (SVC) with Fuzzy Model Reference Learning Controller (FMRLC). The proposed algorithm compensates MG nonlinearity via fuzzy membership functions and inference mechanism imbedded in both controller and inverse model. Hence, MG keeps the desired performance as required at any operating condition. Furthermore, the self-learning capability of the proposed control algorithm compensates for grid parameter’s variation even with inadequate information about load dynamics. A reference model was designed to reject bus voltage disturbance with achievable performance by the proposed fuzzy controller. Three simulations scenarios have been presented to investigate effectiveness of proposed control algorithm in improving steady-state and transient performance of islanded MGs. The first scenario conducted without SVC, second conducted with SVC using PID controller and third conducted using FMRLC algorithm. A comparison for results shows ability of proposed control algorithm to enhance disturbance rejection due to learning process.

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1. Abdelazim T, Malik OP (2005) Power System Stabilizer Based on Model Reference Adaptive Fuzzy Control. Electr Pow Compo Sys 33: 985-998.

2. Abdelsalam AA, Gabbar HA, Sharaf AM (2014) Performance enhancement of hybrid AC/DC microgrid based D-FACTS. Int J Elec Power 63: 382-393.

3. Abdullah A, Zribi M (2009) Model reference control of LPV systems. J Franklin Inst 346: 854-871.

4. Ali S, Qamar S, Khan L (2013) Hybrid Adaptive Recurrent Neurofuzzy Based SVC Control for Damping Inter-Area Oscillations. Middle-East Journal of Scientific Research 16: 536-547.

5. Azad ML, Singh SV, Khursheed A (2014) Improving Voltage Profile Of A Grid, Connected To Wind Farm Using Static Var Compensator. Int J Adv Engineer Technol 7: 1497-1506.

6. Bayat R, Ahmadi H (2013) Artificial Intelligence SVC Based Control of Two Machine Transmission System. I.J. Intelligent Systems and Applications 8: 1-8.

7. Bhole SS, Nigam P (2015) Improvement of Voltage Stability in Power System by Using SVC and STATCOM. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 4: 749-755.

8. Biswas MM, Kamol KD (2011) Voltage Level Improving by Using Static VAR Compensator (SVC). GJRE: (J) General Engineering 11: 12-18.

9. Calderon J, Chamorro H, Ramos G (2012) Advanced SVC Intelligent Control to Improve Power Quality in Microgrids. Alternative Energies and Energy Quality (SIFAE), 2012 IEEE International Symposium on, 1-6.

10. Cerman O (2013) Fuzzy model reference control with adaptation mechanism. Expert Syst Appl 40: 5181-5187.

11. El-dessouky A, Tarbouchi M (2000) Model Reference Adaptive Fuzzy Controller For Induction Motor Using Auto-Attentive Approach. Industrial Electronics, 2000. ISIE 2000. Cholula, Puebla, Mexico.

12. El-dessouky A, Tarbouchi M (2001) Optimized Fuzzy Model Reference Learning Control for Induction Motor Using Genetic Algorithms. Industrial Electronics Society, 2001. IECON '01. The 27th Annual Conference of the IEEE 3: 2038-2043.

13. Fang DZ, Xiaodong Y, Chung TS, et al. (2004) Adaptive Fuzzy-Logic SVC Damping Controller Using Strategy of Oscillation Energy Descent. IEEE Transactions On Power Systems 19: 1414-1421.

14. Farsangi MM, Nezamabadi-pour H, Yong-Hua S, et al. (2007) Placement of SVCs and Selection of Stabilizing Signals in Power Systems. IEEE Transactions On Power Systems 22: 1061-1071.

15. Fernando I, Kwasnicki W, Gole A (1996). Modelling of conventional and advanced Static var compensators in an electromagnetic transient simulation program. Proceedings of the International Symposium on Modern Electric Power Systems 1: 60-70.

16. GENC I, USTA ö (2005) Impacts of Distributed Generators on the Oscillatory Stability of Interconnected Power Systems. Turk J Electr Eng Co 13: 149-161.

17. Goléa N, Goléa A, Benmahammed K (2002). Fuzzy Model Reference Adaptive Control. IEEE Transactions On Fuzzy Systems 10: 436-444.

18. Guang Z, Lijuan Z, Quanhai W, et al. (2007) The Research of Model Reference Adaptive Control of Static Var Compensator (SVC). 7th Internatonal Conference on Power Electronics, 300-304.

19. Hatziargyriou ND, Meliopoulos AP (2002) Distributed energy sources: Technical challenges. IEEE Power Eng Soc Winter Meeting 2: 1017-1022.

20. Hušek P, Cerman O (2013) Fuzzy Model Reference Control with Adaptation of Input Fuzzy Sets. Knowl-Based Syst 49: 116-122.

21. Ian A, Hiskens DJ (1992). Incorporation of SVCS Into Energy Function Methods. IEEE Transaction on Power Systems 7: 133-140.

22. Katiraei F, Iravani R, Hatziargyriou N, et al. (2008) Microgrids management. IEEE Power Energy Mag 6: 54-65.

23. Khan UN, Yan L (2008) Power Swing Phenomena and its Detection and Prevention. 7th EEEIC International Workshop on Environment and Electrical Engineering, 69-72.

24. Kroposki B, Lasseter R, Ise T, et al. (2008) Making microgrids work. IEEE Power and Energy Mag 6: 40-53.

25. Kumar R, Choubey A (2014) Voltage Stability Improvement by using SVC with Fuzzy Logic Controller in Multi-Machine Power System. International Journal of Electrical and Electronics Research 2: 61-66.

26. Layne J, K Passino (1993) Fuzzy model reference learning control for cargo ship steering. IEEE Control Systems Magazine 13: 23-34.

27. Mishra Y, Mishra S, Dong Z (2008) Rough Fuzzy Control of SVC for Power System Stability Enhancement. J Electr Eng Technol 3: 337-345.

28. Noroozian M, Andersson G (2001) A Robust Control Strategy for Shunt and Series Reactive Compensators to Damp Electromechanical Oscillations . IEEE Transactions on Power Delivery 16: 812-817.

29. Procyk T, Mamdani E (1979) A Linguistic Self-Organizing Process Controller. Automatica 15: 15-30.

30. Rogers KM (2009) Power System Control with Distributed Flexible AC Transmission System Devices. Master thesis, University of Illinois at Urbana-Champaign.

31. Sebastiana R, Quesadab J (2006) Distributed control system for frequency control in a isolated wind system. Renewable Energy 31: 285-305.

32. Wang J, Fu C, Zhang Y (2008) SVC Control System Based on Instantaneous Reactive Power Theory and Fuzzy PID. IEEE Transactions On Industrial Electronics 55: 1658-1665.

33. Xiongfei W, Guerrero J, Zhe C (2010) Control of grid interactive AC microgrids. IEEE International Industrial Electronics (ISIE), IEEE International Symposium on : 2211-2216.

34. Yen J, Langari R (1998) Fuzzy Logic: Intelligence, Control, and Information. Prentice Hall.

Copyright Info: © 2016, Hossam Gabbar, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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