Skip to main content

Advertisement

SpringerLink
Log in

Neural sliding-mode load frequency controller design of power systems

  • ISNN 2011
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Load frequency control (LFC) is one of the most profitable ancillary services of power systems. Governor dead band (GDB) nonlinearity is able to deteriorate the LFC performance. In this paper, controller design via a neural sliding-mode method is investigated for the LFC problem of power systems with GDB. Power systems are made up of areas. In each area, a sliding-mode LFC controller is designed by introducing an additional sate, and a RBF neural network is utilized to compensate the GDB nonlinearity of the area. Weight update formula of the RBF network is derived from Lyapunov direct method. By this scheme, not only the update formula is obtained, but also the control system possesses the asymptotic stability. Simulation results illustrate the feasibility and robustness of the presented approach for the LFC problems of single-area and multi-area power systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Kundur P (1994) Power system stability and control. McGraw-Hill, New York

    Google Scholar 

  2. Kothari DP, Nagrath IJ (2003) Modern power system analysis, 3rd edn. McGraw-Hill, Singapore

    Google Scholar 

  3. Ibraheem, Kumar P (2004) A novel approach to the matrix Riccati equation solution: an application to optimal control of interconnected power systems. Electr Power Compon Syst 32(1):33–52

  4. Zribi M, Al-Rashed M, Alrifai M (2005) Adaptive decentralized load frequency control of multi-area power systems. Int J Electr Power Energy Syst 27(8):575–583

    Article  Google Scholar 

  5. Tan W, Xu Z (2009) Robust analysis and design of load frequency controller for power systems. Electr Power Syst Res 79(5):846–853

    Article  Google Scholar 

  6. Tan W (2009) Tuning of PID load frequency controller for power systems. Energy Convers Manage 50(4):1465–1472

    Article  Google Scholar 

  7. Çam E (2007) Application of fuzzy logic for load frequency control of hydroelectrical power plants. Energy Convers Manage 48(4):1281–1288

    Article  Google Scholar 

  8. Tan W (2010) Unified tuning of PID load frequency controller for power systems via IMC. IEEE Trans Power Syst 25(1):341–350

    Article  Google Scholar 

  9. Tan W (2011) Decentralized load frequency controller analysis and tuning for multi-area power systems. Energy Convers Manage 52(5):2015–2023

    Article  Google Scholar 

  10. Liu XJ, Zhan X, Qian DW (2010) Load frequency control considering generation rate constraints. In: Proceedings of 8th world congress on intelligent control and automation, pp 1398–1401

  11. Shayeghi H, Shayanfar HA, Jalili A (2009) Load frequency control strategies: a state-of-the-art survey for the researcher. Energy Convers Manag 50(2):344–353

    Article  Google Scholar 

  12. Utkin VI (1992) Sliding modes in control and optimization. Springer, New York

    Book  MATH  Google Scholar 

  13. Hsu YY, Chan WC (1984) Optimal variable structure controller for the load-frequency control of interconnected hydrothermal power systems. Electr Power Energy Syst 6:221–229

    Article  Google Scholar 

  14. Al-Hamouz ZM, Al-Duwaish HN (2000) A new load frequency variable structure controller using genetic algorithms. Electr Power Syst Res 55(1):1–6

    Article  Google Scholar 

  15. Vrdoljak K, Peric N, Mehmedovic M (2008) Optimal parameters for sliding mode based load-frequency control in power systems. In: Proceedings of international workshop on variable structure systems, pp 331–336

  16. Vrdoljak K, Peric N, Petrovic I (2010) Sliding mode based load-frequency control in power systems. Electr Power Syst Res 80(5):514–527

    Article  Google Scholar 

  17. Tripathy SC, Bhatti TS, Jha CS, Malik OP, Hope GS (1984) Sampled data automatic generation control analysis with reheat steam turbines and governor dead band effects. IEEE Trans Power Apply Syst 103(5):1045–1051

    Article  Google Scholar 

  18. Lu CF, Liu CC (1995) Effect of battery energy storage system on load frequency control considering governor dead-band and generation rate constraint. IEEE Trans Energy Convers 10(3):555–561

    Article  Google Scholar 

  19. Ramakrishna KSS, Bhatti TS (2008) Automatic generation control of single area power system with multi-source power generation. Proc Inst Mech Eng Part A J Power Energy 222(1):1–11

    Article  Google Scholar 

  20. Tripathy SC, Balasubramanian R, Nair PSC (1992) Effect of superconducting magnetic energy storage on automatic generation control considering governor dead-band and boiler dynamics. IEEE Trans Power Syst 7(3):1266–1273

    Article  Google Scholar 

  21. Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3(2):246–257

    Article  Google Scholar 

  22. Wu YL, Sun FC, Zheng JC, Song Q (2010) A robust training algorithm of discrete-time MIMO RNN and application in fault tolerant control of robotic system. Neural Comput Appl 19(7):1013–1027

    Article  Google Scholar 

  23. Chen S, Cowan CFN, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2(2):302–309

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the NSFC Projects under grants No. 60904008, 60874043, 60921061, 61034002, 60975060, the Fundamental Research Funds for the Central Universities under grant No. 09MG19.

Author information

Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, People’s Republic of China

    Dianwei Qian & Xiangjie Liu

  2. State Key Laboratory for Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Dongbin Zhao

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Jianqiang Yi

Authors
  1. Dianwei Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dongbin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianqiang Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiangjie Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dianwei Qian.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Qian, D., Zhao, D., Yi, J. et al. Neural sliding-mode load frequency controller design of power systems. Neural Comput & Applic 22, 279–286 (2013). https://doi.org/10.1007/s00521-011-0709-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0709-0

Keywords

Search

Navigation