Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach

Energies

Dongran Song¹  Yanfang Chen²  Young-Hoon Joo³ 

[1] School of Automation, Central South University, Changsha 410083, China;School of Electronics and Information Engineering, Jiujiang University, Jiujiang 332005, China;School of IT Information and Control Engineering, Kunsan National University, Kunsan 54150, Korea;

关键词: beetle antennae search optimization;    wake propagation;    direct graph;    offshore wind farm;    clustering subset;    graph adaptive pruning;   

DOI  :  10.3390/en14217326

来源: DOAJ

【 摘 要 】

Owing to scale-up and complex wake effects, the centralized control that processes the command from turbines may be unsuitable, as it incurs high communication overhead and computational complexity for a large offshore wind farm (OWF). This paper proposes a novel decentralized non-convex optimization strategy for maxing power conversion of a large OWF based on a modified beetle antennae search (BAS) algorithm. First, an adaptive threshold algorithm which to establish a pruned wake direction graph while preserving the most critical wake propagation relationship among wind turbines are presented. The adaptive graph constraints were used to create wake sub-digraphs that split the wind farm into nearly uncoupled clustering communication subsets. On this basis, a Monte Carlo-based beetle annealing search (MC-BAS) nonlinear optimization strategy was secondly designed to adjust the yaw angles and axial factors for the maximum power conversion of each turbine subgroup. Finally, the simulation results demonstrated that a similar gain could be achieved as a centralized control method at power conversion and reduces the computational cost, allowing it to solve the nonlinear problem and real-time operations of the OWF.

【 授权许可】

Unknown