Model-Free Wind Farm Power Production Optimization using Multi-Resolution Optimized Relative Step Size Random Search

Abstract

This study investigates the performance of Multi-Resolution Optimize Relative Step Size Random Search (MR-ORSSRS) based method in maximizing the total power production of wind farms under circumstances of varying wind directions, turbine failures and non-static wind conditions. With the Horns Rev Wind Farm layout as the basis, the proposed method is compared against the benchmarked Multi-Resolution Stochastic Perturbation Simultaneous Approximation (MR-SPSA). Multi-Resolution (MR) function is further integrated alongside MR-ORSSRS in view of improving the method’s convergence speed. Simulation results hereby show that MR-ORSSRS based method performs MR-SPSA in terms of convergence speed, accuracy and robustness in generating maximum power, even in the cases of deviating wind speeds, turbine failures and varying wind directions.

KEYWORDS:

• Random search
• Model-free
• Wind farm optimization
• Power production
• Stochastic search
• Renewable energy

ACKNOWLEDGEMENTS

Gratitude is expressed to Dr Mohd Ashraf Bin Ahmad as the supervisor for this project, for being extremely supportive throughout the entirety of the research process. His excellent academic guidance and vast knowledge had provided major assistance towards the completion of this research; not to mention, his contribution was beyond the call of duty. Acknowledgement is further extended to my family, especially my mother; who, had given both spiritual and emotional encouragement, as well as financial assistance within the interval of executing this project. Further gratitude is expressed to my colleagues for their friendship and supportive words. Finally, I would like to take this opportunity to thank the Institute of Postgraduate Study (IPS) of UMP for providing me with two years of financial assistance from the Master Research Scheme (MRS).

Additional information

Funding

This work was supported by Universiti Malaysia Pahang.

Notes on contributors

RenHao Mok

RenHao Mok is a PhD student in University Malaysia Pahang. He did researches regarding model-free optimization methods in his studies during master's degree. His interest field of studies are automation, machine learning and IOT.

Mohd Ashraf Ahmad

Mohd Ashraf Ahmad received his first degree in BEng electrical mechatronics and master degree in MEng mechatronics and automatic control from University of Technology Malaysia (UTM) in 2006 and 2008, respectively. In 2015, he received a PhD in informatics (systems science) from Kyoto University. Currently, he is a senior lecturer in the Faculty of Electrical and Electronics Engineering Technology, University Malaysia Pahang (UMP). His current research interests are model-free control, control of mechatronic systems, nonlinear system identification and vibration control. He has been serving as associate editor for the International Journal of Electrical and Computer Engineering since 2016, Applications of Modelling and Simulation since 2017, and Journal of Future Robot Life since 2019. E-mail: [email protected].