Optimal design of IT2FCSbased STATCOM controller applied to power system with wind farms using Taguchi method
Optimal design of IT2FCSbased STATCOM controller applied to power system with wind farms using Taguchi method
 Author(s): YingYi Hong^{ 1} and ManhTuan Nguyen^{ 1}
 DOI: 10.1049/ietgtd.2017.1236
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 Author(s): YingYi Hong^{ 1} and ManhTuan Nguyen^{ 1}


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Affiliations:
1:
Department of Electrical Engineering , Chung Yuan Christian University , Taoyuan 32023 , Taiwan

Affiliations:
1:
Department of Electrical Engineering , Chung Yuan Christian University , Taoyuan 32023 , Taiwan
 Source:
Volume 12, Issue 13,
31
July
2018,
p.
3145 – 3151
DOI: 10.1049/ietgtd.2017.1236 , Print ISSN 17518687, Online ISSN 17518695
The static synchronous compensator (STATCOM) has attracted considerable attention because it can stabilise severe transients that are caused by power system disturbances. This study presents a novel interval type 2 fuzzy control system (IT2FCS)based controller for the STATCOM to stabilise bus voltages that are caused by faults or forced wind farm outages in a smart grid. Two IT2FCSs are presented to tune increments of the proportional integral (PI) controller, which are optimised by the gradient descent method. The IT2 fuzzy rules, involving upper and lower membership functions, result in a fast and stable system response. Since many possible scenarios may arise in the power system, the Taguchi method is used to design experiments using an orthogonal array, in which all scenarios are mutually independent. A power system that consists of a wind farm and STATCOM is studied. Comparative studies show that the proposed method is superior to traditional PI and type 1 FCS methods.
Inspec keywords: fuzzy control; control system synthesis; voltage control; static VAr compensators; power generation control; smart power grids; power system transient stability; wind power plants; power generation faults; PI control; gradient methods
Other keywords: IT2FCSbased STATCOM controller; smart grid; wind farm; power system transient stability; Taguchi method; gradient descent method; static synchronous compensator; power system disturbance; orthogonal array; PI controller; bus voltage stability; interval type 2 fuzzy control system; power system fault
Subjects: Interpolation and function approximation (numerical analysis); Control of electric power systems; Wind power plants; Fuzzy control; Optimisation techniques; Linear algebra (numerical analysis); Power system management, operation and economics; Linear algebra (numerical analysis); Optimisation techniques; Power system control; Stability in control theory; Control system analysis and synthesis methods; Voltage control; Interpolation and function approximation (numerical analysis)
References


1)

1. Hu, R., Hu, W., Chen, Z.: ‘Review of power system stability with high wind power penetration’. Proc. IEEE 41st Annual Conf. Industrial Electronics, Yokohama, Japan, November 2015, pp. 003539–003544.


2)

2. Edrah, M., Lo, K.L., AnayaLara, O.: ‘Impacts of high penetration of DFIG wind turbines on rotor angle stability of power systems’, IEEE Trans. Sustain. Energy, 2015, 6, (3), pp. 759–766.


3)

3. Bu, S.Q., Du, W., Wang, H.F., et al: ‘Probabilistic analysis of smallsignal stability of largescale power systems as affected by penetration of wind generation’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 762–770.


4)

4. Hossain, M.J., Pota, H.R., Mahmud, M.A., et al: ‘Investigation of the impacts of largescale wind power penetration on the angle and voltage stability of power systems’, IEEE Syst. J., 2012, 6, (1), pp. 76–84.


5)

5. Doherty, R., Denny, E., O'Malley, M.: ‘System operation with a significant wind power penetration’. Proc. IEEE Power Engineering Society General Meeting, Denver, CO, USA, June 2004, vol. 1, pp. 1002–1007.


6)

6. Rosyadi, M., Muyeen, S.M., Takahashi, R., et al: ‘Voltage stability control of wind farm using PMSG based variable speed wind turbine’. Proc. Int. Conf. Electrical Machines, Marseille, France, September 2012.


7)

7. Rather, Z.H., Chen, Z., Thøgersen, P., et al: ‘Dynamic reactive power compensation of largescale wind integrated power system’, IEEE Trans. Power Syst., 2015, 30, (5), pp. 2516–2526.


8)

8. Salehi, V., Afsharnia, S., Kahrobaee, S.: ‘Improvement of voltage stability in wind farm connection to distribution network using FACTS devices’. Proc. IEEE 32nd Annual. Conf. Industrial Electronics, IECON'06, Paris, France, November 2006, pp. 4242–4247.


9)

9. Xu, L., Yao, L., Sasse, C.: ‘Comparison of using SVC and STATCOM for wind farm integration’. Proc. IEEE 2006 Int. Conf. Power Syst. Technology (POWERCON 2006), Chongqing, China, October 2006, pp. 1–7.


10)

10. Mahfouz, M.M.A., ElSayed, M.A.H.: ‘Static synchronous compensator sizing for enhancement of fault ridethrough capability and voltage stabilisation of fixed speed wind farms’, IET Renew. Power Gener., 2014, 8, (1), pp. 1–9.


11)

11. Wang, L., Truong, D.N.: ‘Dynamic stability improvement of four paralleloperated PMSGbased offshore wind turbine generators fed to a power system using a STATCOM’, IEEE Trans. Power Deliv., 2013, 28, (1), pp. 111–119.


12)

12. Han, C., Huang, A.Q., Baran, M.E., et al: ‘STATCOM impact study on the integration of a large wind farm into a weak loop power system’, IEEE Trans. Energy Convers., 2008, 23, (1), pp. 226–233.


13)

13. Yasmeena Das, G.T.R.: Fuzzy set theory applications for FACTS devices in grid connected renewable power systems’. Proc. Int. Conf. Devices, Circuits and Systems (ICDCS), Coimbatore, India, March 2016, pp. 245–261.


14)

14. Zadeh, L.A.: ‘The concept of a linguistic variable and its application to approximate reasoning – I’, Inform. Sci., 1975, 8, pp. 199–249.


15)

15. Liang, Q., Mendel, J.M.: ‘Interval type2 fuzzy logic systems: theory and design’, IEEE Trans. Fuzzy Syst., 2000, 8, (5), pp. 535–550.


16)

16. Raju, S.K., Pillai, G.N.: ‘Design and implementation of type2 fuzzy logic controller for DFIGbased wind energy systems in distribution networks’, IEEE Trans. Sustain. Energy, 2016, 7, (1), pp. 345–353.


17)

17. Yassin, H.M., Hanafy, H.H., Hallouda, M.M.: ‘Enhancement lowvoltage ride through capability of permanent magnet synchronous generatorbased wind turbines using interval type2 fuzzy control’, IET Renew. Power Gener., 2016, 10, (3), pp. 339–348.


18)

18. Sakalli, A., Kumbasar, T., Yesilm, E., et al: ‘Analysis of the performances of type1, selftuning type1 and interval type2 fuzzy PID controllers on the magnetic levitation system’. Proc. IEEE Inter. Conf. Fuzzy Systems, Beijing, China, July 2014, pp. 1859–1866.


19)

19. Roy, R.K.: ‘Design of experiments using the Taguchi approach’ (John Wiley & Sons, New York, 2001).


20)

20. Liu, D., Cai, Y.: ‘The Taguchi method for solving the economic dispatch problem with nonsmooth cost functions’, IEEE Trans. Power Syst., 2005, 20, (4), pp. 2006–2014.


21)

21. Hong, Y.Y., Lin, F.J., Yu, T.H.: ‘The Taguchi methodbased probabilistic load flow studies considering uncertain renewables and loads’, IET Renew. Power Gener., 2016, 10, (2), pp. 221–227e.


22)

22. Mendel, J.M.: ‘Uncertain rulebased fuzzy logic systems: introduction and new directions’ (Prentice Hall PTR, Upper Saddle River, NJ, 2001).


23)

23. Mondal, D., Chakrabarti, A., Sengupta, A.: ‘Power system small signal stability analysis and control’ (Academic Press, Cambridge, MA, USA, 2014), pp. 41–83.


24)

24. Optimization Toolbox User's Guide, MATLAB, R2017a, MathWorks Inc..


25)

25. Chen, G., Pham, T.T.: ‘Introduction to fuzzy sets, fuzzy logic and fuzzy control systems’ (CRC Press, Boca Raton, Florida, USA, 2001), p. 215.


26)

26. Aliev, R.A., Guirimov, B.G.: ‘Type2 fuzzy neural networks and their applications’ (Springer, New York, NY, USA, 2014), p. 129.


27)

27. AlFandi, M., Jaradat, M.A.K., Sardahi, Y.: ‘Optimal PIfuzzy logic controller of glucose concentration using genetic algorithm’, Int. J. Knowl.Based Intell. Eng. Syst., 2011, 15, pp. 99–117.


28)

28. Chowdhury, M.A., Sayem, A.H.M., Shen, W., et al: ‘Robust active disturbance rejection controller design to improve lowvoltage ridethrough capability of doubly fed induction generator wind farms’, IET Renew. Power Gener., 2015, 9, (8), pp. 961–969.


29)

29. Bayem, H., Capely, L., Dufourd, F., et al: ‘Probabilistic study of the maximum penetration rate of renewable energy in an island network’. Proc. IEEE PowerTech, Bucharest, Romania, June 2009.


1)