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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)

Abstract

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.

Keywords

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

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

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