Article
Hybrid Wind Turbine Towers Optimization with a Parallel
Updated Particle Swarm Algorithm
Zeyu Li
1,
* , Hongbing Chen
2
, Bin Xu
3,4
and Hanbin Ge
3,5
Citation: Li, Z.; Chen, H.; Xu, B.; Ge,
H. Hybrid Wind Turbine Towers
Optimization with a Parallel Updated
Particle Swarm Algorithm. Appl. Sci.
2021, 11, 8683. https://doi.org/
10.3390/app11188683
Academic Editor: Nikos D. Lagaros
Received: 14 August 2021
Accepted: 12 September 2021
Published: 17 September 2021
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1
College of Civil Engineering, Hunan University, Changsha 410082, China
2
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083,
China; hongbingchen@ustb.edu.cn
3
College of Civil Engineering, Huaqiao University, Xiamen 361021, China; binxu@hqu.edu.cn (B.X.);
gehanbin@meijo-u.ac.jp (H.G.)
4
Key Laboratory for Intelligent Infrastructure and Monitoring of Fujian Province, Huaqiao University,
Xiamen 361021, China
5
Department of Civil Engineering, Meijo University, Nagoya 468-8502, Japan
* Correspondence: lizeyu@hnu.edu.cn
Abstract:
The prestressed concrete–steel hybrid (PCSH) wind turbine tower, characterized by re-
placing the lower part of the traditional full-height steel tube wind turbine tower with a prestressed
concrete (PC) segment, provides a potential alterative solution to transport difficulties and risks
associated with traditional steel towers in mountainous areas. This paper proposes an optimization
approach with a parallel updated particle swarm optimization (PUPSO) algorithm which aims at
minimizing the objective function of the levelized cost of energy (LCOE) of the PCSH wind turbine
towers in a life cycle perspective which represents the direct investments, labor costs, machinery costs,
and the maintenance costs. Based on the constraints required by relevant specifications and industry
standards, the geometry of a PCSH wind turbine tower for a 2 MW wind turbine is optimized using
the proposed approach. The dimensions of the PCSH wind turbine tower are treated as optimization
variables in the PUPSO algorithm. Results show that the optimized PCSH wind turbine tower can
be an economic alternative for wind farms with lower LCOE requirements. In addition, compared
with the traditional particle swarm optimization (PSO) algorithm and UPSO algorithm, the proposed
PUPSO algorithm can enhance the optimization computation efficiency by about 60–110%.
Keywords:
prestressed concrete–steel hybrid (PCSH) wind turbine tower; optimal design; parallel
updated particle swarm optimization (PUPSO) algorithm; wind; earthquake; levelized cost of
energy (LCOE)
1. Introduction
The wind turbine tower, as the structure supporting the wind turbine, represents a
highly significant component of wind turbine systems and accounts for approximately
30% of the overall investment in onshore installations [
1
]. With the increase in unit power
capacity of wind turbines, the heights of wind turbine towers have increased for the
purpose of capturing wind energy efficiently, as wind profiles are strong and steady at
higher elevations [
2
–
4
]. In recent years, wind turbine towers with a height of over 100 m
have been widely employed in practice alongside increasing investment [
5
]. Many wind
farms have been developed or are under construction in mountainous areas in the mainland
of China after decades of wind farm development in plain areas. The transportation of
segmental steel tubes and long blades to the top of mountains is a challenging task with
risks. Moreover, the construction of temporary transportation roads with large turning
radii in mountains leads to additional investment and environmental destruction [
6
]. The
traditional steel-tubular wind turbine tower systems are typical soft supporting systems,
and it is hard to meet the stiffness requirements of large capacity wind turbines due to the
limitation of steel-tube diameter transportation.
Appl. Sci. 2021, 11, 8683. https://doi.org/10.3390/app11188683 https://www.mdpi.com/journal/applsci
