Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: Aug 1, 2015

Abstract

In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance.

Authors

  • Soheil Ganjefar
    Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Shahid Fahmideh Street, P.O. Box 65178-38683, Hamedan, Iran. Electronic address: s_ganjefar@basu.ac.ir.
  • Morteza Tofighi
    Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Shahid Fahmideh Street, P.O. Box 65178-38683, Hamedan, Iran. Electronic address: tofighi_morteza@yahoo.com.
  • Hamidreza Karami
    Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Shahid Fahmideh Street, P.O. Box 65178-38683, Hamedan, Iran. Electronic address: hamidr.karami@gmail.com.

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