Performance enhancement of the artificial neural network-based reinforcement learning for wind turbine yaw control

[1]  H. Rahimi,et al.  An engineering model for wind turbines under yawed conditions derived from high fidelity models , 2018 .

[2]  Christos Mademlis,et al.  Active yaw control in a horizontal axis wind system without requiring wind direction measurement , 2016 .

[3]  Rachid Beguenane,et al.  Implementation of Sliding Mode Control System for Generator and Grid Sides Control of Wind Energy Conversion System , 2016, IEEE Transactions on Sustainable Energy.

[4]  Ligang Wu,et al.  Nonlinear Control of Variable Speed Wind Turbines via Fuzzy Techniques , 2017, IEEE Access.

[5]  Hui Li,et al.  A Stochastic Digital Implementation of a Neural Network Controller for Small Wind Turbine Systems , 2006, IEEE Transactions on Power Electronics.

[6]  C. S. Hilas,et al.  Pareto Optimal Design of Dual-Band Base Station Antenna Arrays Using Multi-Objective Particle Swarm Optimization With Fitness Sharing , 2009, IEEE Transactions on Magnetics.

[7]  Jason R. Marden,et al.  Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study , 2016 .

[8]  Mario Rizo,et al.  A Novel Multifrequency Current Reference Calculation to Mitigate Active Power Fluctuations , 2018, IEEE Transactions on Industrial Electronics.

[9]  K. Strunz,et al.  A review of hybrid renewable/alternative energy systems for electric power generation: Configurations, control and applications , 2011, 2012 IEEE Power and Energy Society General Meeting.

[10]  Dirk Söffker,et al.  State-of-the-art in wind turbine control: Trends and challenges , 2016 .

[11]  Aristide F. Massardo,et al.  Microturbine/Fuel-Cell Coupling for High-Efficiency Electrical-Power Generation , 2002 .

[12]  Davide Astolfi,et al.  Wind Turbine Power Curve Upgrades , 2018 .

[13]  Aitor Saenz-Aguirre,et al.  Optimal Wind Turbine Operation by Artificial Neural Network-Based Active Gurney Flap Flow Control , 2019, Sustainability.

[14]  J. Chow,et al.  Windfarm Power Optimization Using Yaw Angle Control , 2017, IEEE Transactions on Sustainable Energy.

[15]  Robert Babuska,et al.  Experience Replay for Real-Time Reinforcement Learning Control , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  Roohollah Fadaeinedjad,et al.  A New Model for PMSG-Based Wind Turbine With Yaw Control , 2013, IEEE Transactions on Energy Conversion.

[17]  Joachim Peinke,et al.  Investigation of the validity of BEM for simulation of wind turbines in complex load cases and comparison with experiment and CFD , 2016 .

[18]  Alessandro Pisano,et al.  Receding Horizon Adaptive Second-Order Sliding Mode Control for Doubly-Fed Induction Generator Based Wind Turbine , 2017, IEEE Transactions on Control Systems Technology.

[19]  Jian Yang,et al.  Maximum power extraction for wind turbines through a novel yaw control solution using predicted wind directions , 2018 .

[20]  Davide Astolfi,et al.  Innovative methods for wind turbine power curve upgrade assessment , 2018 .

[21]  Johan Meyers,et al.  Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization , 2018 .

[22]  Manoj Kumar Tiwari,et al.  Interactive Particle Swarm: A Pareto-Adaptive Metaheuristic to Multiobjective Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[23]  Aitor Saenz-Aguirre,et al.  Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control , 2019, Energies.

[24]  Xavier Gandibleux,et al.  A survey and annotated bibliography of multiobjective combinatorial optimization , 2000, OR Spectr..

[25]  Hamidreza Jafarnejadsani,et al.  Adaptive Control of a Variable-Speed Variable-Pitch Wind Turbine Using Radial-Basis Function Neural Network , 2013, IEEE Transactions on Control Systems Technology.

[26]  Andrew Kusiak,et al.  Optimization of Wind Turbine Performance With Data-Driven Models , 2010, IEEE Transactions on Sustainable Energy.

[27]  Dewen Hu,et al.  Multiobjective Reinforcement Learning: A Comprehensive Overview , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[28]  Ekaitz Zulueta,et al.  Systematic modeling of photovoltaic modules based on artificial neural networks , 2016 .

[29]  Young-Man Kim.  Robust data driven H-infinity control for wind turbine , 2016, J. Frankl. Inst..

[30]  Leandro dos Santos Coelho,et al.  A Multiobjective Gaussian Particle Swarm Approach Applied to Electromagnetic Optimization , 2010, IEEE Transactions on Magnetics.

[31]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[32]  Hamed Habibi,et al.  Adaptive PID Control of Wind Turbines for Power Regulation With Unknown Control Direction and Actuator Faults , 2018, IEEE Access.

[33]  Mohamed Abid,et al.  A Fuzzy-PI control to extract an optimal power from wind turbine , 2013 .

[34]  Shiyou Yang,et al.  A particle swarm optimization-based method for multiobjective design optimizations , 2005, IEEE Transactions on Magnetics.