Hybrid Algorithm Combing Genetic Algorithm With Evolution Strategy for Antenna Design

Choi, Kyung K.; Jang, Donghyeok; Kang, Seong-In; Lee, Jeong-Hyeok; Chung, Tae-Kyung; Kim, Hyeong-Seok

doi:10.1109/tmag.2015.2486043

Cited by 115 publications

(63 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A mean normal radial stress, qr is applied to the outside surface of the rotor leads to a radial deflection, (12) where Bg is the air-gap flux density, w = wm for surface-mounted Nd-Fe-B generator and w = wp for flux-concentrating ferrite generator. In this paper, the structural dimensions of the arms and yoke are varied to meet the deflection criteria.…”

Section: Generator Structural Modelmentioning

confidence: 99%

“…In the flux-concentrating ferrite generator case, additional aluminum cylinder thickness is added. Equation (12) shows that the loads on the structure are strongly dependent on the electromagnetic model.…”

Section: Generator Structural Modelmentioning

confidence: 99%

“…They are suitable for both constrained and unconstrained optimization problems. Although GAs are good at searching global optima over an entire problem region, the speed of convergence to the optimal point can be slow [12]. On the other hand, deterministic optimization methods like the pattern search (PS) are very efficient for local searching [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Optimization of Generators for Offshore Direct Drive Wind Turbines

McDonald

Bhuiyan

2017

IEEE Trans. Energy Convers.

View full text Add to dashboard Cite

The Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.1 Abstract-The objective of this paper is to optimize direct drive permanent magnet synchronous generators for offshore direct drive wind turbines in order to reduce the cost of energy. A 6MW wind turbine design is assumed and parametric electromagnetic and structural generator models are introduced for a surface-mounted magnet generator topology (using magnets with high BHmax) and a flux-concentrating variant (using magnets with lower BHmax). These are optimized using a hybrid Genetic Algorithm and Pattern Search process and the results show that the surface-mounted permanent magnet generator produces the lower cost of energy. The choice of objective function is addressed and it is found that a simplified metric incorporating generator cost and losses proxy produces similar designs to a full cost of energy calculation. Further steps to improve the quality of the model include the effect of generator mass on the design and cost of the turbine tower and foundation, which can add €0.4m to the turbine cost. Further optimizations are carried out to show the impacts of magnetic material costs (doubling this leads to a €1.1/MWh increase in cost of energy) and generator diameter limits (increasing the upper limit from 6m to 8m leads to a 0.9% drop in cost of energy) have on the choice of optimum independent variables.Index Terms-Cost of energy, direct drive wind turbine, optimization, permanent magnet generator, structural model, tower and foundation. I. INTRODUCTIONgrowing proportion of offshore wind turbine designs are now based on directly driven permanent magnet synchronous generators. Direct drive machines can offer higher reliability and reduced maintenance cost because of the omission of the gearbox from the drive train [1]. Some of the downsides of these generators include their large size (due to the high torque rating), requirements for large quantities of rare earth permanent magnets and the significant generator structures required to maintain the small air-gap clearance against the large attraction forces between the rotor and the stator [2]. The generator designer needs to deliver a number of performance characteristics including high efficiency, low power losses at part load, high availability, low machine mass, reduced volume and lower material and manufacturing costs. Normally the designers employ some element of optimization to achieve the best balance of these aspects [3].The main purpose of this paper is to examine the process of optimizing large, low speed generators for offshore direct drive wind turbines, exploring the different objective functions that a A. McDonald and N. A. Bhuiyan are with the Institute of Energy and Environment, Department of Electronic and Electri...

show abstract

Section: Generator Structural Modelmentioning

confidence: 99%

Section: Generator Structural Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the Optimization of Generators for Offshore Direct Drive Wind Turbines

McDonald

Bhuiyan

2017

IEEE Trans. Energy Convers.

View full text Add to dashboard Cite

show abstract

“…The ever-increasing demands of modern wireless communications, including 4G/5G, wireless sensor networks, and Internet of Things (IoT), require antenna designs to handle multiple objectives, e.g., wideband or multi-band, high gain or efficiency, compact size, etc. In this circumstance, automated antenna optimization based on multi-objective evolutionary algorithms (MOEAs), such as genetic algorithm (GA) [1], particle swarm optimization (PSO) [2], and multi-objective optimization algorithm based on decomposition (MOEA/D) [3], provide a new path for antenna designers because of their strong capabilities of simultaneously handling multiple design objectives and optimizing multiple design parameters. However, the direct application of MOEAs to antenna optimizations may be computationally intensive in the multi-parameter antenna designs since a large number of full-wave EM simulations are usually involved in the optimization process [4].…”

Section: Introductionmentioning

confidence: 99%

Fast Multi-Objective Antenna Optimization Based on RBF Neural Network Surrogate Model Optimized by Improved PSO Algorithm

Dong

Wang

2019

Applied Sciences

View full text Add to dashboard Cite

In this paper, a radial basis function neural network (RBFNN) surrogate model optimized by an improved particle swarm optimization (PSO) algorithm is developed to reduce the computation cost of traditional antenna design methods which rely on high-fidelity electromagnetic (EM) simulations. Considering parameters adjustment and update mechanism simultaneously, two modifications are proposed in this improved PSO. First, time-varying learning factors are designed to balance exploration and exploitation ability of particles in the search space. Second, the local best information is added to the updating process of particles except for personal and global best information for better population diversity. The improved PSO is applied to train RBFNN for determining optimal network parameters. As a result, the constructed improved PSO-RBFNN model can be used as a surrogate model for antenna performance prediction with better network generalization capability. By integrating the improved PSO-RBFNN surrogate model with multi-objective evolutionary algorithms (MOEAs), a fast multi-objective antenna optimization framework for multi-parameter antenna structures is then established. Finally, a Pareto-optimal planar miniaturized multiband antenna design is presented, demonstrating that the proposed model provides better prediction performance and considerable computational savings compared to those previously published approaches.

show abstract

“…Genetic Algorithms (GA) have been proved to be good and trustworthy methods of solving such problems. Although GAs are good at searching global optima over an entire problem region, the speed of convergence to the optimal point can be slow [6]. A hybrid algorithm combining Genetic and Pattern search (good for local search) can be used to solve this problem [5], [7].…”

Section: Introductionmentioning

confidence: 99%