Hybrid Wind Turbine Towers Optimization with a Parallel Updated Particle Swarm Algorithm

The prestressed concrete–steel hybrid (PCSH) wind turbine tower, characterized by replacing 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%.

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Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096511666180629152127 ◽

2019 ◽

Vol 12 (3) ◽

pp. 277-283

Author(s):

Jiatang Cheng ◽

Yan Xiong

Keyword(s):

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Wind Turbine ◽

Bp Neural Network ◽

Quantum Particle ◽

Particle Swarm ◽

Wind Turbine Gearbox ◽

Bp Network ◽

Swarm Optimization ◽

Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

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Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an Improved Version of Particle Swarm Optimization

Elektrichestvo ◽

10.24160/0013-5380-2021-12-15-27 ◽

2021 ◽

pp. 15-27

Author(s):

Mamdouh Kamaleldin AHMED ◽

◽

Mohamed Hassan OSMAN ◽

Nikolay V. KOROVKIN ◽

◽

...

Keyword(s):

Particle Swarm Optimization ◽

Power Systems ◽

Distributed Generation ◽

Particle Swarm ◽

Optimal Location ◽

Particle Swarm Algorithm ◽

Power Losses ◽

Negative Effects ◽

Swarm Optimization ◽

Distributed Generations

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

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Introduction of New Anti-dumping Algorithm in EU under WTO Law System Based on Particle Swarm Optimization

Tobacco Regulatory Science ◽

10.18001/trs.7.5.2.22 ◽

2021 ◽

Vol 7 (5) ◽

pp. 4558-4567

Author(s):

Wenwen Deng

Keyword(s):

Particle Swarm Optimization ◽

Operation Mode ◽

Particle Swarm ◽

Algorithm Analysis ◽

Particle Swarm Algorithm ◽

Swarm Optimization ◽

Law System ◽

Incomplete System ◽

Swarm Algorithm ◽

The Eu

Objectives: Anti dumping new algorithm is an innovative ability based on the WTO legal system, which has made an important contribution to the economic development of the EU system. Methods: At present, the operation mode of new antidumping algorithm has some defects, such as structure confusion and incomplete system implementation, which affects the development progress of EU economic growth. Results: Based on the above problems, in this paper, particle swarm algorithm is introduced, based on the optimization analysis of the website structure of the new antidumping algorithm, through the independent screening analysis of particle swarm optimization, combining the WTO economy with the EU status theory, Conclusion: the paper obtains the optimized anti-dumping innovation scheme on the basis of particle swarm algorithm analysis, and finally passes the input test. The feasibility of the scheme is established.

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Optimal Power Flow with TCSC and TCPS Modeling using Craziness and Turbulent Crazy Particle Swarm Optimization

International Journal of Swarm Intelligence Research ◽

10.4018/jsir.2010070103 ◽

2010 ◽

Vol 1 (3) ◽

pp. 34-50 ◽

Cited By ~ 8

Author(s):

P. K. Roy ◽

S. P. Ghoshal ◽

S. S. Thakur

Keyword(s):

Particle Swarm Optimization ◽

Power Flow ◽

Optimal Power Flow ◽

Particle Swarm ◽

Solution Quality ◽

Swarm Optimization ◽

Facts Devices ◽

Computation Efficiency ◽

Optimal Power ◽

Real Coded Genetic Algorithm

This paper presents two new Particle swarm optimization methods to solve optimal power flow (OPF) in power system incorporating flexible AC transmission systems (FACTS). Two types of FACTS devices, thyristor-controlled series capacitor (TCSC) and thyristor controlled phase shifting (TCPS), are considered. In this paper, the problems of OPF with FACTS are solved by using particle swarm optimization with the inertia weight approach (PSOIWA), real coded genetic algorithm (RGA), craziness based particle swarm optimization (CRPSO), and turbulent crazy particle swarm optimization (TRPSO). The proposed methods are implemented on modified IEEE 30-bus system for four different cases. The simulation results show better solution quality and computation efficiency of TRPSO and CRPSO algorithms over PSOIWA and RGA. The study also shows that FACTS devices are capable of providing an economically attractive solution to OPF problems.

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Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

Sustainability ◽

10.3390/su10124445 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4445 ◽

Cited By ~ 4

Author(s):

Lejun Ma ◽

Huan Wang ◽

Baohong Lu ◽

Changjun Qi

Keyword(s):

Particle Swarm Optimization ◽

Computational Efficiency ◽

Particle Swarm ◽

Pso Algorithm ◽

Optimal Operation ◽

Computational Time ◽

Particle Swarm Algorithm ◽

Swarm Optimization ◽

Swarm Algorithm ◽

Low Efficiency

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

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Particle Swarm Optimization-Based Secure Computation Efficiency Maximization in a Power Beacon-Assisted Wireless-Powered Mobile Edge Computing NOMA System

Energies ◽

10.3390/en13215540 ◽

2020 ◽

Vol 13 (21) ◽

pp. 5540 ◽

Cited By ~ 1

Author(s):

Carla E. Garcia ◽

Mario R. Camana ◽

Insoo Koo

Keyword(s):

Particle Swarm Optimization ◽

Multiple Access ◽

Particle Swarm ◽

Secure Computation ◽

Edge Computing ◽

Computation Offloading ◽

Processing Unit ◽

Mobile Edge Computing ◽

Swarm Optimization ◽

Computation Efficiency

In this paper, we aim to provide reliable user connectivity and enhanced security for computation task offloading. Physical layer security is studied in a wireless-powered non-orthogonal multiple access (NOMA) mobile edge computing (MEC) system with a nonlinear energy-harvesting (EH) user and a power beacon (PB) in the presence of an eavesdropper. To further provide a friendly environment resource allocation design, wireless power transfer (WPT) is applied. The secure computation efficiency (SCE) problem is solved by jointly optimizing the transmission power, the time allocations for energy transfer, the computation time, and the central processing unit (CPU) frequency in the NOMA-enabled MEC system. The problem is non-convex and challenging to solve because of the complexity of the objective function in meeting constraints that ensure the required quality of service, such as the minimum value of computed bits, limitations on total energy consumed by users, maximum CPU frequency, and minimum harvested energy and computation offloading times. Therefore, in this paper, a low-complexity particle swarm optimization (PSO)-based algorithm is proposed to solve this optimization problem. For comparison purposes, time division multiple access and fully offloading baseline schemes are investigated. Finally, simulation results demonstrate the superiority of the proposed approach over baseline schemes.

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