PSO-GSA based fuzzy sliding mode controller for DFIG-based wind turbine.

In this paper an optimal fuzzy sliding mode control (FSMC) strategy is introduced for doubly fed induction generator-based wind turbine (DFIG-based WT). The control objective is to ensure power extraction maximization and null stator reactive power regulation according to the grid requirements. To this end, the sliding mode control (SMC) technique is combined with a simple fuzzy inference system to avoid the undesirable chattering phenomenon inherent to the conventional SMC. The proposed FSMC strategy is derived using the Lyapunov approach to guarantee the closed loop system's stability. To obtain optimal control performances, the membership function parameters of the incorporated fuzzy system are tuned using an improved population-based optimization algorithm, which consists on the combination of particle swarm optimization (PSO) and gravitational search algorithm (GSA). Finally, the performances of the proposed control scheme, called PSO-GSA based FSMC are evaluated in comparison with PSO based FSMC and GSA based FSMC systems.

[1]  Ouassima Akhrif,et al.  Design of a nonlinear robust adaptive controller for a Grid-connected Doubly-Fed Induction Generator Wind turbine , 2010, 18th Mediterranean Conference on Control and Automation, MED'10.

[2]  Mohamed Abid,et al.  A Robust Sliding Mode Control Applied To The Double Fed Induction Machine , 2012 .

[3]  Cheng Lu,et al.  Adaptive fractional order sliding mode controller with neural estimator , 2018, J. Frankl. Inst..

[4]  Kuldip S. Rattan,et al.  Genetic multi-stage fuzzy PID controller with a fuzzy switch , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[5]  Zhicheng Ji,et al.  A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints , 2014 .

[6]  Abdesselem Boulkroune,et al.  Adaptive Fuzzy Control of Doubly-Fed Induction Machine , 2014 .

[7]  Rong-Jong Wai,et al.  Fuzzy Sliding-Mode Control Using Adaptive Tuning Technique , 2007, IEEE Transactions on Industrial Electronics.

[8]  Cheng Lu,et al.  Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Djilani Ben Attous,et al.  Optimal tuning of PI controller using PSO optimization for indirect power control for DFIG based wind turbine with MPPT , 2014, Int. J. Syst. Assur. Eng. Manag..

[10]  Chia-Ju Wu,et al.  Genetic Auto-Tuning and Rule Reduction of Fuzzy PID Controllers , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[11]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[12]  Jang-Mok Kim,et al.  Feedback linearization control of Doubly-fed induction generator under an unbalanced voltage , 2011, 8th International Conference on Power Electronics - ECCE Asia.

[13]  Weihua Gui,et al.  Passivity-Based Asynchronous Sliding Mode Control for Delayed Singular Markovian Jump Systems , 2018, IEEE Transactions on Automatic Control.

[14]  Seul Jung,et al.  Improvement of Tracking Control of a Sliding Mode Controller for Robot Manipulators by a Neural Network , 2018 .

[15]  S. Mirjalili,et al.  A new hybrid PSOGSA algorithm for function optimization , 2010, 2010 International Conference on Computer and Information Application.

[16]  Jie Wu,et al.  Integral variable structure direct torque control of doubly fed induction generator , 2011 .

[17]  Ligang Wu,et al.  Event-triggered sliding mode control of stochastic systems via output feedback , 2017, Autom..

[18]  Muwaffaq I. Alomoush,et al.  Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator , 2018 .

[19]  Juntao Fei,et al.  Adaptive backstepping fuzzy sliding mode vibration control of flexible structure , 2018 .

[20]  A.E. Leon,et al.  An Adaptive Nonlinear Controller for DFIM-Based Wind Energy Conversion Systems , 2008, IEEE Transactions on Energy Conversion.

[21]  Michael R. Lyu,et al.  A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training , 2007, Appl. Math. Comput..

[22]  Shixi Hou,et al.  Dynamic global proportional integral derivative sliding mode control using radial basis function neural compensator for three-phase active power filter , 2018, Trans. Inst. Meas. Control.

[23]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[24]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[25]  Abdesselem Boulkroune,et al.  Adaptive fuzzy vector control for a doubly-fed induction motor , 2015, Neurocomputing.

[26]  F. Poitiers,et al.  Advanced control of a doubly-fed induction generator for wind energy conversion , 2009 .

[27]  K. Premalatha,et al.  Hybrid PSO and GA models for Document Clustering , 2010 .

[28]  Ligang Wu,et al.  Disturbance-Observer-Based Control for Air Management of PEM Fuel Cell Systems via Sliding Mode Technique , 2019, IEEE Transactions on Control Systems Technology.

[29]  T. Ahmed-Ali,et al.  Second-Order Sliding Mode Control of a Doubly Fed Induction Generator Driven Wind Turbine , 2012, IEEE Transactions on Energy Conversion.

[30]  Yancai Xiao,et al.  The Study of Fuzzy Proportional Integral Controllers Based on Improved Particle Swarm Optimization for Permanent Magnet Direct Drive Wind Turbine Converters , 2016 .

[31]  C. Evangelista,et al.  Multivariable 2-sliding mode control for a wind energy system based on a double fed induction generator , 2012 .

[32]  Weihua Gui,et al.  A Novel Asynchronous Control for Artificial Delayed Markovian Jump Systems via Output Feedback Sliding Mode Approach , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[33]  Saeide Sheikhpour,et al.  A hybrid Gravitational search algorithm — Genetic algorithm for neural network training , 2013, 2013 21st Iranian Conference on Electrical Engineering (ICEE).

[34]  Korany R. Mahmoud,et al.  Parallel Implementation of Hybrid Gsa-Nm Algorithm for Adaptive Beam-Forming Applications , 2014 .

[35]  Pierre-Yves Glorennec,et al.  Tuning fuzzy PID controllers using ant colony optimization , 2009, 2009 17th Mediterranean Conference on Control and Automation.

[36]  Juntao Fei,et al.  Adaptive fuzzy-neural-network based on RBFNN control for active power filter , 2019, Int. J. Mach. Learn. Cybern..

[37]  A. Djahbar,et al.  Variable Structure Control of a Doubly Fed Induction Generator for Wind Energy Conversion Systems , 2014 .

[38]  Abdesselem Boulkroune,et al.  Fuzzy generalized projective synchronization of incommensurate fractional-order chaotic systems , 2016, Neurocomputing.

[39]  Abdesselem Boulkroune,et al.  Adaptive fuzzy controller for multivariable nonlinear state time-varying delay systems subject to input nonlinearities , 2011, Fuzzy Sets Syst..

[40]  Hussein Thani Rishag,et al.  Improvement The DFIG Active Power with Variable Speed Wind using Particle Swarm Optimization , 2016 .

[41]  Andrea Tilli,et al.  Indirect stator flux-oriented output feedback control of a doubly fed induction machine , 2003, IEEE Trans. Control. Syst. Technol..