Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement. (November 2015)

• Record Type: Journal Article
• Authors: Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza
• Is Part Of: Neural networks. Volume 71(2015:Nov.)
• Page Start: 172
• Page End: 181
• Publication Date: 2015-11
• Subjects: Indirect adaptive fuzzy wavelet neural controller -- Power system stabilizer -- Quantum neural network; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2015.07.010
• Languages: English
• ISSNs: 0893-6080

Abstract

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.