Robust fractional order controller based on improved particle swarm optimization algorithm for the wind turbine equipped with a doubly fed asynchronous machine

In this paper, an improvement of the particle swarm optimization algorithm is proposed. The aim of this algorithm is to determine the optimal parameters of a robust fractional order controller which guarantees stability and robustness of required nominal performances. Controllers with fractional order are first obtained by a previous transformation of the multi-objective optimization problem into an equivalent single-optimization problem, and then solved by the proposed improved particle swarm optimization algorithm. In order to examine the stability and performance robustness, this controller is applied on an ill-conditioned wind turbine equipped with a doubly fed asynchronous machine, where the system dynamics is modeled by an unstructured output multiplicative uncertainty model. The simulation results show the effectiveness of the proposed synthesis method, where the control is compared (for the same design frequency-domain specifications) for both the robust fractional order controller such as that designed through the standard particle swarm optimization algorithm and that obtained by resolving the multi-objective optimization problem.

[1]  Johan Driesen,et al.  Wind turbine modelling approaches for dynamic power system simulations , 2004 .

[2]  E. Yaz.  Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[3]  Derek A. Linkens,et al.  Fuzzy-neural control: principles, algorithms and applications , 1995 .

[4]  Pierre Apkarian,et al.  A Spectral Quadratic-SDP Method with Applications to Fixed-Order H2 and H∞ Synthesis , 2004, Eur. J. Control.

[5]  Kamran Behdinan,et al.  Particle swarm approach for structural design optimization , 2007 .

[6]  Arkadi Nemirovski,et al.  Lmi Control Toolbox For Use With Matlab , 2014 .

[7]  S. L. Shah,et al.  Robust multivariable control of a binary distillation column , 1983 .

[8]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[9]  Johan Driesen,et al.  Equivalent Transfer Function for a Variable-speed Wind Turbine in Power System Dynamic Simulations , 2005 .

[10]  YangQuan Chen,et al.  Tuning and auto-tuning of fractional order controllers for industry applications , 2008 .

[11]  Petko H. Petkov,et al.  Robust control design with MATLAB , 2005 .

[12]  A. Megretski.  6.245: Multivariable Control Systems , 2004 .

[13]  Leandro dos Santos Coelho,et al.  Model-free adaptive control optimization using a chaotic particle swarm approach , 2009 .

[14]  Qiuying Zou,et al.  GREENHOUSE AIR TEMPERATURE CONTROL USING THE PARTICLE SWARM OPTIMIZATION ALGORITHM , 2007 .

[15]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[16]  Tayeb Allaoui,et al.  Robust control of doubly fed induction generator for stand-alone applications , 2010 .

[17]  Pierre Apkarian,et al.  Nonsmooth H∞ synthesis , 2005, IEEE Trans. Autom. Control..

[18]  P. Apkarian,et al.  Robust control via concave minimization local and global algorithms , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[19]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[20]  Singiresu S. Rao.  Engineering Optimization : Theory and Practice , 2010 .

[21]  Toshiharu Sugie,et al.  Fixed-structure H∞ controller synthesis: A meta-heuristic approach using simple constrained particle swarm optimization , 2009, Autom..

[22]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[23]  Mohamed Zribi,et al.  Adaptive decentralized load frequency control of multi-area power systems , 2005 .