UMP Institutional Repository

Performance evaluation of random search based methods on model-free wind farm control

Hao, Mok Ren and Mohd Ashraf, Ahmad and Raja Mohd Taufika, Raja Ismail and Ahmad Nor Kasruddin, Nasir (2018) Performance evaluation of random search based methods on model-free wind farm control. In: Intelligent Manufacturing & Mechatronics: Proceedings of Symposium, 29 January 2018, Pekan, Pahang, Malaysia. Lecture Notes in Mechanical Engineering . Springer Singapore, Singapore, pp. 657-670. ISBN 9789811087875

[img] Pdf
book43. Performance Evaluation of Random Search Based Methods.pdf
Restricted to Repository staff only

Download (288kB) | Request a copy
[img]
Preview
Pdf
book43.1 Performance Evaluation of Random Search Based Methods.pdf

Download (89kB) | Preview

Abstract

This paper investigates the performance of Sequential Random Search (SRS), Fixed Step Size Random search (FSSRS), Optimized Relative Step Size Random Search (ORSSRS) and Adaptive Step Size Random Search (ASSRS) methods on maximizing offshore wind farms power production. The RS based methods are used to tune the control parameter of each turbine to its optimum until the wind farm total power production is maximized. The validation of this investigation is performed using the Horns Rev wind farm model with turbulence interaction between turbines. Simulation results show that Optimized Relative Step Size Random Search (ORSSRS) produces higher total power production as compared to other types of RS based methods.

Item Type: Book Section
Additional Information: Index by Scopus
Uncontrolled Keywords: Random search; Model-free; Wind farm optimization; Power production; Stochastic search; Renewable energy
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 06 Aug 2018 08:30
Last Modified: 06 Aug 2018 08:30
URI: http://umpir.ump.edu.my/id/eprint/21638
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item