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

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.

[1]  Rong-Jong Wai.  Development of new training algorithms for neuro-wavelet systems on the robust control of induction servo motor drive , 2002, IEEE Trans. Ind. Electron..

[2]  Mahmoud Moghavvemi,et al.  Damping of low-frequency oscillations and improving power system stability via auto-tuned PI stabilizer using Takagi-Sugeno fuzzy logic , 2012 .

[3]  Zakariya Al-Hamouz,et al.  A neural network based adaptive sliding mode controller: Application to a power system stabilizer , 2011 .

[4]  A. Chatterjee,et al.  Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer , 2011 .

[5]  Sung Jin Yoo,et al.  Stable Predictive Control of Chaotic Systems Using Self-Recurrent Wavelet Neural Network , 2005 .

[6]  S. M. Radaideh,et al.  Design of power system stabilizers using two level fuzzy and adaptive neuro-fuzzy inference systems , 2012 .

[7]  Han Xu-li,et al.  The multidimensional function approximation based on constructive wavelet RBF neural network , 2011 .

[8]  M. L. Kothari,et al.  Intelligent dual input power system stabilizer , 2003 .

[9]  Issarachai Ngamroo,et al.  Robust coordinated control of electrolyzer and PSS for stabilization of microgrid based on PID-based mixed H2/H∞ control , 2012 .

[10]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[11]  Nobuyuki Matsui,et al.  Qubit neural network and its learning efficiency , 2005, Neural Computing & Applications.

[12]  Soheil Ganjefar,et al.  Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network , 2014 .

[13]  Abdel Latif Elshafei,et al.  Damping inter-area modes of oscillation using an adaptive fuzzy power system stabilizer , 2010 .

[14]  Amin Khodabakhshian,et al.  Robust decentralized multi-machine power system stabilizer design using quantitative feedback theory , 2012 .

[15]  Afrooz Ebadat,et al.  New fuzzy wavelet network for modeling and control:The modeling approach , 2011 .

[16]  Fayez F. M. El-Sousy,et al.  Robust wavelet-neural-network sliding-mode control system for permanent magnet synchronous motor drive , 2011 .

[17]  M. Azizur Rahman,et al.  A Novel Neuro-Wavelet-Based Self-Tuned Wavelet Controller for IPM Motor Drives , 2010 .

[18]  Yilu Liu,et al.  Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers , 2008 .

[19]  Sakti Prasad Ghoshal,et al.  Comparative seeker and bio-inspired fuzzy logic controllers for power system stabilizers , 2011 .

[20]  Nobuyuki Matsui,et al.  A Multi-Layerd Feed-Forward Network Based on Qubit Neuron Model , 2002 .

[21]  Nobuyuki Matsui,et al.  Image Compression by Layered Quantum Neural Networks , 2002, Neural Processing Letters.

[22]  Okyay Kaynak,et al.  Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study , 2008, IEEE Transactions on Industrial Electronics.

[23]  Ping Zhang,et al.  Fuzzy Wavelet Neural Networks for City Electric Energy Consumption Forecasting , 2012 .

[24]  Qinghua Zhang,et al.  Wavelet networks , 1992, IEEE Trans. Neural Networks.

[25]  S. Ganjefar,et al.  Damping of inter-area oscillations by Self-Recurrent Wavelet Neural Adaptive controlled SSSC , 2012, 20th Iranian Conference on Electrical Engineering (ICEE2012).

[26]  Om P. Malik,et al.  Identification of synchronous generators using adaptive wavelet networks , 2005 .

[27]  Abdelaziz Hamzaoui,et al.  Type-2 fuzzy based adaptive synergetic power system control , 2012 .

[28]  Takashi Hiyama,et al.  Robust PID based power system stabiliser: Design and real-time implementation , 2011 .

[29]  Hossam E.A. Talaat,et al.  Design and experimental investigation of a decentralized GA-optimized neuro-fuzzy power system stabilizer , 2010 .