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

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 optimizatio...

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Autores principales:	Yanfang Chen, Young-Hoon Joo, Dongran Song
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	beetle antennae search optimization wake propagation direct graph offshore wind farm clustering subset graph adaptive pruning Technology T
Acceso en línea:	https://doaj.org/article/ce2da6524cd24eaf82f6aa29bd2406f5
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spelling	oai:doaj.org-article:ce2da6524cd24eaf82f6aa29bd2406f52021-11-11T16:03:04ZModified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach10.3390/en142173261996-1073https://doaj.org/article/ce2da6524cd24eaf82f6aa29bd2406f52021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7326https://doaj.org/toc/1996-1073Owing 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.Yanfang ChenYoung-Hoon JooDongran SongMDPI AGarticlebeetle antennae search optimizationwake propagationdirect graphoffshore wind farmclustering subsetgraph adaptive pruningTechnologyTENEnergies, Vol 14, Iss 7326, p 7326 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	beetle antennae search optimization wake propagation direct graph offshore wind farm clustering subset graph adaptive pruning Technology T
spellingShingle	beetle antennae search optimization wake propagation direct graph offshore wind farm clustering subset graph adaptive pruning Technology T Yanfang Chen Young-Hoon Joo Dongran Song Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach
description	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.
format	article
author	Yanfang Chen Young-Hoon Joo Dongran Song
author_facet	Yanfang Chen Young-Hoon Joo Dongran Song
author_sort	Yanfang Chen
title	Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach
title_short	Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach
title_full	Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach
title_fullStr	Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach
title_full_unstemmed	Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach
title_sort	modified beetle annealing search (bas) optimization strategy for maxing wind farm power through an adaptive wake digraph clustering approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ce2da6524cd24eaf82f6aa29bd2406f5
work_keys_str_mv	AT yanfangchen modifiedbeetleannealingsearchbasoptimizationstrategyformaxingwindfarmpowerthroughanadaptivewakedigraphclusteringapproach AT younghoonjoo modifiedbeetleannealingsearchbasoptimizationstrategyformaxingwindfarmpowerthroughanadaptivewakedigraphclusteringapproach AT dongransong modifiedbeetleannealingsearchbasoptimizationstrategyformaxingwindfarmpowerthroughanadaptivewakedigraphclusteringapproach
_version_	1718432440490393600

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

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