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...
| Autori principali: | , , |
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| Natura: | Conference or Workshop Item |
| Lingua: | English English |
| Pubblicazione: |
Institute of Electrical and Electronics Engineers Inc.
2022
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| Soggetti: | |
| Accesso online: | 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 |
| Riassunto: | 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. |
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