Dynamic programming strategy in optimal controller design for a wind turbine system

Abstract

In this article, an optimal controller is proposed to extract maximum wind energy from the available wind speed. The very fluctuating nature of wind speed has made the process of extracting wind energy complicated. To extract and optimize such a complicated system, a dynamic programming-based optimal control approach is well suited to non-linear wind turbine system models and restrictions. The performance of this control method is tested via MATLAB software. The outcomes of the system have a 7 up to 8.5 tip speed ratio optimal value and the aerodynamic efficiency has a 0.411 conversion value. With the proposed controller, the energy captured is improved by 13.841% and 1.15% for piecewise step input and randomly generated wind speed respectively compared to standard torque control. The lower percentage improvement, in this case, is due to the limited range of the wind speed value given for the simulation. Generally, the optimal control method is globally maximizing wind energy capture for each time step via forward and backward recursion of dynamic programming rather than normal torque control.

Keywords:

• Dynamic programming
• Wind energy
• Optimal control
• Optimization

Reviewing Editor:

• Ai Qingsong, Wuhan University of Technology, China

Subjects:

• Technology
• Engineering & Technology
• Biomedical Engineering
• Biosensors
• Medical Devices
• Electrical & Electronic Engineering
• Intelligent Systems
• Power Electronics
• Instrumentation, Measurement & Testing
• Power Engineering
• Systems & Controls
• Power & Energy
• Renewable Energy
• Transport & Vehicle Engineering

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Abibual Abate Mitaw

Abibual Abate Mitaw received the B.Sc. degree in electrical and computer engineering (industrial control engineering) from Hawassa University, Hawassa, Ethiopia, in 2015, and the M.Sc. degree in electrical and computer engineering (control science) from Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia, in 2019. He is currently a Senior Lecturer with the B Woldia Institute of Technology, Woldia University. He has published two articles in the Scopus Web of Science Journals. His research interests include robotics, systems, control, electric vehicles, renewable energy, smart microgrids, energy systems, artificial intelligent, and machine learning.

Abrham Tadesse Kassie

Abrham Tadesse Kassie received the B.Sc. degree in electrical and computer engineering (industrial control engineering) from Hawassa University, Hawassa, Ethiopia, in 2015, and the M.Sc. degree in electrical and computer engineering (control and instrumentation engineering) from Addis Ababa Science and Technology University, Addis Ababa, Ethiopia, in 2019. He is currently a Senior Lecturer with the Bahir Dar Institute of Technology, Bahir Dar University. He serves as the Chair for the Control and Instrumentation Engineering, Bahir Dar Institute of Technology. He has published four articles in the Scopus Web of Science Journals. His research interests include robotics, systems, control, electric vehicles, renewable energy, smart microgrids, energy systems, artificial intelligent, and machine learning.

Dereje Shiferaw Negash

Dereje Shiferaw Negash received the M.Tech. and Ph.D. degrees from the Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, India, in 2009 and 2011, respectively. He is currently an Assistant Professor with the School of Electrical and Computer Engineering, Addis Ababa University. His research interests include neural networks, fuzzy logic systems, genetic algorithms, and application of AI in nonlinear control and robotics.