Power flow control in wind energy conversion systems using PSO optimized adaptive sliding mode control

In this paper, particle swarm optimisation (PSO) is used to optimize an Adaptive Sliding Mode Controller (ASMC) used for active and reactive power control for the most widely used Wind Energy Conversion System (WECS) based in a doubly-fed induction generator (DFIG). Simulations performed on Matlab/Simulink software compare results obtained with and without PSO tuned parameters to highlight the performance of the approach.

[1]  Jiabing Hu,et al.  Direct Active and Reactive Power Regulation of Grid-Connected DC/AC Converters Using Sliding Mode Control Approach , 2011, IEEE Transactions on Power Electronics.

[2]  Sharon Ravichandran,et al.  Coordinated controller design of grid connected DFIG based wind turbine using response surface methodology and NSGA II , 2014 .

[3]  Jingqing Han,et al.  From PID to Active Disturbance Rejection Control , 2009, IEEE Trans. Ind. Electron..

[4]  Rabah Babouri,et al.  Integration of Doubly Fed Induction Generator Entirely Interfaced with Network in a Wind Energy Conversion System , 2013 .

[5]  A. Peer Fathima,et al.  GENETIC ALGORITHM BASED PID CONTROLLER FOR A TWO-AREA DEREGULATED POWER SYSTEM ALONG WITH DFIG UNIT , 2015 .

[6]  K. M. Muttaqi,et al.  Direct power control of DFIG based wind turbine based on wind speed estimation and particle swarm optimization , 2015, 2015 Australasian Universities Power Engineering Conference (AUPEC).

[7]  Afef Fekih,et al.  A Fault-Tolerant Control Paradigm for Microgrid-Connected Wind Energy Systems , 2018, IEEE Systems Journal.

[8]  Adel A.A. El-Gammal.  Optimal Design of PID Controller for Doubly-Fed Induction Generator-Based Wave Energy Conversion System Using Multi-Objective Particle Swarm Optimization , 2014 .

[9]  B. Mazari,et al.  Artificial Intelligence control applied in wind energy conversion system , 2014, 2014 International Renewable and Sustainable Energy Conference (IRSEC).

[10]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[11]  R. K. Patnaik,et al.  Adaptive terminal sliding mode power control of DFIG based wind energy conversion system for stability enhancement , 2016 .

[12]  S. M. H. Hosseini,et al.  Optimization of Direct Power Control in Doubly-Fed Induction Machine (DFIM) using PSO Algorithm , 2013 .

[13]  Tamou Nasser,et al.  A Linear Active Disturbance Rejection Control applied for DFIG based Wind Energy Conversion System , 2013 .

[14]  A. Betka,et al.  Optimal tracking and robust power control of the DFIG wind turbine , 2013 .

[15]  Mohamed Cherkaoui,et al.  Active and Reactive Power Robust Control of Doubly Fed Induction Generator Wind Turbine to Satisfy New Grid Codes , 2016, AECIA.

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

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

[18]  Rachid Taleb,et al.  Comparative Study of Robust Control Strategies for a Dfig-Based Wind Turbine , 2016 .