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Application of intelligence-based predictive scheme to load-frequency control in a two-area interconnected power system

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Abstract

This paper describes an application of intelligence-based predictive scheme to load-frequency control (LFC) in a two-area interconnected power system. In this investigation, at first, a dynamic model of the present system has to be considered and subsequently an efficient control scheme which is organized based on Takagi-Sugeno-Kang (TSK) fuzzy-based scheme and linear generalized predictive control (LGPC) scheme needs to be developed. In the control scheme proposed, frequency deviation versus load electrical power variation could efficiently be dealt with, at each instant of time. In conclusion, in order to validate the effectiveness of the proposed control scheme, the whole of outcomes are simulated and compared with those obtained using a nonlinear GPC (NLGPC), as a benchmark approach, which is implemented based on the Wiener model of this power system. The validity of the proposed control scheme is tangibly verified in comparison with the previous one.

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References

  1. Prasanth BV, Kumar SVJ (2008) Load frequency control for a two area interconnected power system using robust genetic algorithm controller. J Theor Appl Inf Technol, 1204–1212

  2. Prasanth BV, Kumar SVJ (2009) Robust fuzzy Load frequency controller for a two area interconnected power system. J Theor Appl Inf Technol, 242–252

  3. Lee HJ (2006) Robust load frequency control for uncertain non linear power systems: A fuzzy logic approach. Inf Sci 176:3520–3537

    Article  MATH  Google Scholar 

  4. Erlugrul C, Ilhan K (2005) Fuzzy logic controller in interconnected electrical power systems for frequency control. Electr Power Syst 37:542–549

    Google Scholar 

  5. Chang C, Fu W (1997) Area load frequency control using fuzzy gain scheduling of PI controllers. Electr Power Syst 2:145–152

    Article  Google Scholar 

  6. Rerkpreedapong D, Hasanovic A, Feliachi A (2003) Robust load frequency control using genetic algorithms and linear matrix inequalities. IEEE Trans Power Syst 18(2):855–861

    Article  Google Scholar 

  7. Rebours Y, Kirschen D, Trotignon M, Rossignol S (2007) A survey of frequency and voltage control ancillary services-part I: technical features. IEEE Trans Power Syst 22(1):350–357

    Article  Google Scholar 

  8. Park N, Lee D, Hyun D (2007) A power-control scheme with constant switching frequency in class-D inverter for induction-heating. IEEE Trans Ind Electron 54(3):1252–1260

    Article  Google Scholar 

  9. Iwanski G, Koczara W (2007) Sensorless direct voltage control of the stand-alone slip-ring induction generator. IEEE Trans Ind Electron 54(2):1237–1239

    Article  Google Scholar 

  10. Murakami A, Yokoyama A, Tada Y (2006) Basic study on battery capacity evaluation for load frequency control (LFC) in power system with a large penetration of wind power generation. Trans Inst Electr Eng Jpn 126(2):236–242

    Google Scholar 

  11. Vrdoljak K, Peric N, Petrovic I (2007) Estimation and prediction in load frequency control. In: Proc of international conference on energy and electrical drives, pp 785–790

  12. Shayeghi H, Shayanfar H, Malik O (2007) Robust decentralized neural networks based LFC in a deregulated power system. Electr Power Syst Res 77(3–4):241–251

    Article  Google Scholar 

  13. Flores A, Saez D, Araya J, Berenguel M, Cipriano A (2005) Fuzzy predictive control of a solar power plant. IEEE Trans Fuzzy Syst 1:58–68

    Article  Google Scholar 

  14. Rebours Y, Kirschen D, Trotignon M, Rossignol S (2007) A survey of frequency and voltage control ancillary services-part II: economic features. IEEE Trans Power Syst 22(1):358–366

    Article  Google Scholar 

  15. Huang H, Hsieh C, Chen K, Nat S (2006) Adaptive frequency control technique for enhancing transient performance of DC-DC converters. In: Proc of solid state circuits conference

  16. Mohamed Y, El-Saadany E (2007) An improved deadbeat current control scheme with a novel adaptive self-tuning load model for a three-phase PWM voltage-source inverter. IEEE Trans Ind Electron 54(2):747–759

    Article  Google Scholar 

  17. Mazinan AH, Sadati N (2009) Fuzzy predictive control based multiple models strategy for a tubular heat exchanger. Appl Intell (in press). Online available Feb 4, 2009. doi:10.1007/s10489-009-0163-1

  18. Mazinan AH, Sadati N (2009) An intelligent multiple models based predictive control scheme with its application to industrial tubular heat exchanger system. Appl Intell (in press). Online available Jun 16, 2009. doi:10.1007/s10489-009-0185-8

  19. Mazinan AH, Sadati N, Ahmadi-Noubari H (2009) A case study for fuzzy adaptive multiple models predictive control strategy. In: Proc of IEEE world symposium on industrial electronics, pp 1172–1177

  20. Mazinan AH, Sadati N (2008) Fuzzy multiple models predictive control of tubular heat exchanger. In: Proc of IEEE world congress on computational intelligence, pp 1845–1852

  21. Mazinan AH, Sadati N (2008) Multiple modeling and fuzzy predictive control of a tubular heat exchanger system. Trans Syst Control 3:249–258

    Google Scholar 

  22. Mazinan AH, Sadati N (2008) Fuzzy multiple modeling and fuzzy predictive control of a tubular heat exchanger system. In: International conference on application of electrical engineering, pp 77–81

  23. Mazinan AH, Sadati N (2008) Fuzzy multiple modeling and fuzzy predictive control of a tubular heat exchanger system. In: International conference on robotics, control and manufacturing technology, pp 93–97

  24. Mazinan AH, Sadati N (2010) On the application of fuzzy predictive control based on multiple models strategy to a tubular heat exchanger system. Trans Inst Meas Control, pp 1–24. doi:10.1177/0142331209345153

  25. Mazinan AH, Kazemi MF (2010) A new approach to intelligent model based predictive control scheme. Intelligent Information Management, Scientific Research Publisher, pp 14–20. doi:10.4236/iim.2010.21002

  26. Mazinan AH, Kazemi MF, Shahbazi H (2010) Innovations in generalized predictive control using TSK fuzzy-based approach. In: IEEE international symposium on industrial electronics, Italy, July 2010

  27. Mazinan AH, Kazemi MF (2010) Notes on intelligence based model predictive control scheme: A case study. In: IEEE intelligent systems conference, UK, July 2010

  28. Mazinan AH, Kazemi MF (2010) Design of an intelligent control scheme based on predictive theory. In: IEEE international conference on networking, sensing and control, USA, April 2010, pp 85–88

  29. Mazinan AH, Kazemi MF (2010) An efficient solution to load-frequency control using fuzzy-based predictive scheme in a two-area interconnected power system. In: The international conference on computer and automation engineering, Singapore, February 2010, vol 1, pp 289–293

  30. Olamaei J, Mazinan AH, Arefi A, Niknam T (2010) A hybrid evolutionary algorithm based on ACO and SA for distribution feeder reconfiguration. In: The international conference on computer and automation engineering, Singapore, vol 4, pp 265–269

  31. Sousa JMDC, Kaymak U (2001) Model prediction control using fuzzy decision functions. IEEE Trans Syst Man Cybern—Part B: Cybern 1:54–65

    Article  Google Scholar 

  32. Zhang Y, Chi Z, Liu X, Wang X (2007) A novel fuzzy compensation multi-class support vector machine. Appl Intell 27:21–28

    Article  Google Scholar 

  33. Chen S, Chen S (2005) A prioritized information fusion method for handling fuzzy decision-making problems. Appl Intell 22:219–232

    Article  MATH  Google Scholar 

  34. Huang S, Tan KK, Lee TH (2002) Applied predictive control. Springer, London

    Google Scholar 

  35. Clarke DW (1988) Application of generalized predictive control to industrial processes. IEEE Control Syst Mag 8:49–55

    Article  Google Scholar 

  36. Sousa JM (2000) Optimization issues in predictive control with fuzzy objective functions. Int J Intell Syst 15:879–899

    Article  MATH  Google Scholar 

  37. Zamarreno JM, Vega P (1999) Neural predictive control application to a highly non-linear system. Eng Appl Artif Intell 12:149–158

    Article  Google Scholar 

  38. Gadkar KG, Doyle FJ III, Crowley TJ, Varner JD (2003) Cybernetic model predictive control of a continuous bioreactor with cell recycle. Biotechnol Prog 19:1487–1497

    Article  Google Scholar 

  39. Saha P, Krishnan SH, Rao VSR, Patwardhan SC (2004) Modeling and predictive control of MIMO nonlinear systems using Wiener-Laguerre models. Chem Eng Commun 8:1083–1120

    Google Scholar 

  40. Franco E, Sacone S, Parisini T (2004) Practically stable nonlinear receding-horizon control of multi-model systems. In: Proc of IEEE conference on decision and control, vol 3, pp 3241–3246

  41. Rueda A, Cristea S, Prada CD, Keyser RD (2005) Non-linear predictive control for a distillation column. In: Proc of 44th IEEE conference on decision and control, pp 5156–5161

  42. Arefi MM, Montazeri A, Poshtan J, Jahed-Motlagh MR (2006) Nonlinear model predictive control of chemical processes with a Wiener identification approach. In: Proc of IEEE conference on industrial technology, pp 1735–1740

  43. Abraham RJ, Das D, Patra A (2007) Automatic generation control of an interconnected hydrothermal power system considering superconducting magnetic energy storage. Int J Electr Power Energy Syst 29:571–579

    Article  Google Scholar 

  44. Ngamroo I (2005) An optimization technique of robust load frequency stabilizer for superconducting magnetic energy storage. Energy Convers Manag 46:3060–3090

    Article  Google Scholar 

  45. Ngamroo I (2005) Robust decentralized frequency stabilizers design for SMES taking into consideration system uncertainties. Electr Power Syst Res 74(2):281–292

    Article  Google Scholar 

  46. Ibraheem A, Kumar P, Kothari DP (2005) Recent philosophies of automatic generation control strategies in power systems. IEEE Trans Power Syst 20(1):346–357

    Article  Google Scholar 

  47. Kocaarslan I, Cam E (2005) Fuzzy logic controller in interconnected electrical power systems for load-frequency control. Int J Electr Power Energy Syst 27:542–549

    Article  Google Scholar 

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Authors and Affiliations

  1. Electrical Engineering Department, Islamic Azad University (IAU), South Tehran Branch, Tehran, Iran

    A. H. Mazinan

  2. Computer Engineering Department, Sharif University of Technology, Tehran, Iran

    A. H. Hosseini

  3. Control Department, Engineering Research Center, Tehran, Iran

    A. H. Hosseini

Authors
  1. A. H. Mazinan
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  2. A. H. Hosseini
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Correspondence to A. H. Mazinan.

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Mazinan, A.H., Hosseini, A.H. Application of intelligence-based predictive scheme to load-frequency control in a two-area interconnected power system. Appl Intell 35, 457–468 (2011). https://doi.org/10.1007/s10489-010-0236-1

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  • DOI: https://doi.org/10.1007/s10489-010-0236-1

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