Using adaptive safe experimentation dynamics algorithm for maximizing wind farm power production

This research presents a model-free strategy for increasing wind farm power generation based on the Adaptive Safe Experimentation Dynamics Algorithm (ASEDA). The ASEDA method is an improved version of the Safe Experimentation Dynamics (SED) algorithm that modifies the current tuning variable to resp...

全面介紹

書目詳細資料
Main Authors: Mohd Ashraf, Ahmad, Jui, Julakha Jahan, Mohd Riduwan, Ghazali
格式: Conference or Workshop Item
語言:English
English
出版: Institute of Electrical and Electronics Engineers Inc. 2022
主題:
在線閱讀:http://umpir.ump.edu.my/id/eprint/42113/1/Using%20adaptive%20safe%20experimentation%20dynamics%20algorithm.pdf
http://umpir.ump.edu.my/id/eprint/42113/2/Using%20adaptive%20safe%20experimentation%20dynamics%20algorithm%20for%20maximizing%20wind%20farm%20power%20production_ABS.pdf
實物特徵
總結:This research presents a model-free strategy for increasing wind farm power generation based on the Adaptive Safe Experimentation Dynamics Algorithm (ASEDA). The ASEDA method is an improved version of the Safe Experimentation Dynamics (SED) algorithm that modifies the current tuning variable to respond to the changes in the objective function. The convergence accuracy is predicted to be enhanced further by adding the adaptive element to the modified SED equation. The ASEDA-based technique is used to determine the ideal control parameter for each turbine to maximize a wind farm's total power generation. A single single-row wind farm prototype with turbulence coupling among turbines is employed to validate the proposed approach. Simulation findings show that the ASEDA-based approach provides more total power generation than the original SED technique.