Robust and Adaptive Load Frequency Control of Multi-area Power Networks with System Parametric Uncertainties Using Temporal Difference Based MLP Neural Networks

  • Farzan Rashidi
  • Mehran Rashidi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3192)


In this paper a robust and adaptive Temporal Difference learning based MLP (TDMLP) neural network for power system Load Frequency Control (LFC) is presented. Power systems, such as other industrial processes, are nonlinear and have parametric uncertainties that for controller design had to take the uncertainties into account. For this reason, in the design of LFC controller the idea of TDMLP neural network is being used. Some simulations with two interconnections are given to illustrate proposed method. Results on interconnected power system show that the proposed method not only is robust to increasing of load perturbations and operating point variations, but also it gives good dynamic response compared with traditional controllers. It guarantees the stability of the overall system even in the presence of generation rate constraint (GRC). To evaluate the usefulness of proposed method we compare the response of this method with RBF neural network and PID controller. Simulation results show the TDMLP has the better control performance than RBF neural network and PID controller.


Power System Learning Agent Load Disturbance Good Dynamic Response Load Perturbation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sasaki, T., Kadoya, T., Enomoto, K.: Study on Load Frequency Control Using Redox Flow Batteries. IEEE Trans. on Power Systems, 1–8 (2003)Google Scholar
  2. 2.
    Moon, Y.-H., Ryu, H.-S., Lee, J.-G., Kim, S.: Power system load frequency control using noise-tolerable PID feedback. In: IEEE International Symposium on Industrial Electronics, vol. 3, pp. 1714–1718 (2001)Google Scholar
  3. 3.
    el-din Azzam, M.: An optimal approach to robust controller design for load-frequency control. In: Transmission and Distribution Conference and Exhibition, vol. 1, pp. 180–183 (2002)Google Scholar
  4. 4.
    Jafari-Harandi, M., Bathee, S.M.T.: Decentralized variable-structure and fuzzy logic load frequency control of multi-area power systems. In: Fuzzy Logic Symposium, Malaysia (1997)Google Scholar
  5. 5.
    Wang, Y., Zhou, R., Wen, C.: Robust controller design for power system load frequency control. In: First IEEE Conference on Control Application, vol. 2, pp. 642–646 (1992)Google Scholar
  6. 6.
    Talaq, J., Al-Basri, F.: Adaptive fuzzy gain scheduling for load frequency control. IEEE Transactions on Power Systems 14(1), 145–150 (1999)CrossRefGoogle Scholar
  7. 7.
    Beaufays, F., et al.: Application of neural networks to load frequency control in power system. Neural Networks 7(1), 183–194 (1994)CrossRefGoogle Scholar
  8. 8.
    Shayeghi, Shayanfar, H.A.: Application of ANN Technique for Interconnected Power System Load Frequency Control. In: Internationa Power System Conference, PSC 2004, Tehran, pp. 33–40 (2003)Google Scholar
  9. 9.
    Dayan, P.: Temporal Differences: TD(λ) for General λ. Machine Learning (1991) (in press)Google Scholar
  10. 10.
    Lippmann, R.P.: An Introduction to Computing with Neural Nets. IEEE ASSP Magazine, 4–22 (1987)Google Scholar
  11. 11.
    Sutton, R.S.: Temporal Credit Assignment in Reinforcement Learning. Doctoral Dissertation, Department of Computer and Information Science, University of Massachusetts, Amherst (1984)Google Scholar
  12. 12.
    Sutton, R.S., Barto, A.G.: A Temporal Difference Model of Classical Conditioning. In: Proceedings of the Ninth Annual Conference of the Cognitive Science Society, pp. 355–378. Lawrence Erlbaum, Seattle (1987)Google Scholar
  13. 13.
    Perlovsky, L.I.: Emotions, Learning and control. In: Proc. of IEEE Int. symp. On Intelligent control/Intelligent systems and semiotics, Cambridge MA, pp. 132–137 (1999)Google Scholar
  14. 14.
    Sutton, R.S.: Learning to Predict by the Methods of Temporal Differences. Machine Learning 3, 9–44 (1988)Google Scholar
  15. 15.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by Error Propagation. In: Parallel Distributed Processing (PDP): Exploration in Microstructure of Recognition, ch. 8, vol. 1, MIT Press, Cambridge (1986)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Farzan Rashidi
    • 1
  • Mehran Rashidi
    • 1
  1. 1.Control Research DepartmentEngineering Research InstituteTehranIran

Personalised recommendations