• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.48-57
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• 2009

This paper presents optimal design of direct-driven PM wind generator using MADS (Mesh Adaptive Direct Search). Optimal design of the direct-driven PM Wind Generator, combined with MADS and FEM (Finite Element Method), has been performed to maximize the Annual Energy Production (AEP) over the whole wind speed characterized by the statistical model of the wind speed distribution. In particular, the newly applied MADS contributes to reducing the computation time when compared with Genetic Algorithm (GA) implemented with the parallel computing method.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1549-1557
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• 2018

Optimizing the design of large-scale electric machines based on nonlinear finite element analysis (FEA) requires longer computation time than other applications of FEA, mainly due to the huge size of the machines. This paper addresses a new region decision method (RDM) with mesh adaptive direct search (MADS) for the optimal design of wind generators in order to reduce the computation time. The validity of the proposed algorithm is evaluated using Rastrigin and Goldstein-Price benchmark function. Moreover, the algorithm is employed for the optimal design of a 5.6MW interior permanent magnet synchronous generator to minimize the torque ripple. Additionally, mechanical stress analysis as well as electromagnetic field analysis have been implemented to prevent breakdown caused by large centrifugal forces of the modified design.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.216-222
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• 2012

Newly proposed RG-MADS (Randomly Guided Mesh Adaptive Direct Search) has been applied to the optimal design of Interior Permanent Magnet Synchronous Motor (IPMSM) which has the distinctive features of magnetic saturation. RG-MADS, advanced from classical MADS algorithm, has the superiority in computational time and reliable convergence accuracy to the optimal solution, thus it is appropriate to the optimal design of IPMSM coupled with time-consuming Finite Element Analysis (FEA), necessary to the nonlinear magnetic application for better accuracy. Effectiveness of RG-MADS has been verified through the well-known benchmark-functions beforehand. In addition, the proposed RG-MADS has been applied to the optimal design of IPMSM aiming at maximizing the Maximum Torque Per Ampere (MTPA), which is regarded as representative design goal of IPMSM.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.615_616
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• 2009

유한요소해석(Finite Element Analysis)을 통한 풍력발전기 최적설계시, 해석 특성상 발생하는 막대한 소요시간의 개선이 필요하다. 본 논문에서는 연간 에너지 생산량(Annual Energy Production : AEP)의 최대화를 목표로 GA(Genetic Algorithm)와 MADS(Mesh Adaptive Direct Search)를 결합한 혼합 알고리즘을 이용하여 최적설계를 수행하였다. 또한, 혼합 알고리즘과 병렬분산 유전알고리즘을 이용한 최적설계의 해석 소요시간을 비교 및 검토하였다.

• International Journal of Fluid Machinery and Systems
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• v.8 no.3
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• pp.209-219
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• 2015

A robust multi-fidelity optimization methodology has been developed, focusing on efficiently handling industrial runner design of hydraulic Francis turbines. The computational task is split between low- and high-fidelity phases in order to properly balance the CFD cost and required accuracy in different design stages. In the low-fidelity phase, a physics-based surrogate optimization loop manages a large number of iterative optimization evaluations. Two derivative-free optimization methods use an inviscid flow solver as a physics-based surrogate to obtain the main characteristics of a good design in a relatively fast iterative process. The case study of a runner design for a low-head Francis turbine indicates advantages of integrating two derivative-free optimization algorithms with different local- and global search capabilities.

• Journal of Power Electronics
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• v.18 no.4
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• pp.1037-1050
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• 2018

This paper presents a novel reconfigurable selective harmonic elimination technique to control harmonics over a wide range of Modulation Indexes (MI) in Multi-Level Inverter (MLI). In the proposed method, the region of the MI is divided into various sectors and expressions are formulated with different switching patterns for each of the sectors. A memetic BBO-MAS (Biogeography Based Optimization - Mesh Adaptive direct Search) optimization algorithm is proposed for solving the Selective Harmonic Elimination - Pulse Width Modulation (SHE-PWM) technique. An experimental prototype is developed using a Field Programmable Gate Array (FPGA) and their FFT spectrums are analyzed over a wide range of MI using a fluke power logger. Simulation and experimental results have validated the performance of the proposed optimization algorithms and the reconfigurable SHE-PWM technique. Further, the sensitivity of the harmonics has been analyzed considering non-integer variations in the magnitude of the input DC sources.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.647-648
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• 2008

풍력발전기 최적 설계시, 해석특성상 발생하는 막대한 계산 시간문제를 개선하기 위해, 본 논문에서는 최대 연간 에너지 생산량(AEP)을 위한 풍력발전기 최적설계를 빠른 탐색 기법인 MADS(Mesh Adaptive Direct Search)를 기반으로 최적화를 수행하였다. 또한, MADS와, 병렬 분산컴퓨팅 시스템과 결합된 유전알고리즘(Genetic Algorithms)간의 최적화 수행시간을 비교하였다.

• Proceedings of the KIEE Conference
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• 2008.10c
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• pp.12-14
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• 2008

전기기기 최적설계 프로세스는 목적함수 계산을 위한 특성해석 부분과 최적화 알고리즘 부분으로 구분된다. 여기서 수렴시간 중 대부분을 특성해석에서 차지하므로 수렴시간을 줄이기 위해서는 목적함수 호출을 최소화하기 위한 최적화 알고리즘의 전략적 선택이 요구되며, 아울러 전기기기 설계가 가지는 Multimodal한 특성을 충분히 고려해 줄 필요가 있다. 본 논문은 특성 함수 호출의 최소화를 위해 지역탐색 기법인 MADS(Mesh Adaptive Direct Search)와 전역 최적해 탐색 가능성을 높이기 위해 확률론적 최적화 알고리즘인 G.A(Genetic Algorithms)를 유기적으로 결합하여 전기기기 최적설계에 요구되는 전반적인 특징을 포괄한 미미틱 알고리즘을 구현하였으며, 구현된 알고리즘을 테스트 함수에 적용하여 수렴결과를 나타내었다.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.6-8
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• 2009

This paper presents the novel implementation of memetic algorithm with GA (Genetic Algorithm) and MADS (Mesh Adaptive Direct Search), which is applied for optimal design methodology of electric machine. This hybrid algorithm has been developed for obtaining the global optimum rapidly, which is effective for optimal design of electric machine with many local optima and much longer computation time. In particular, the proposed memetic algorithm has been forwarded to optimal design of direct-driven PM wind generator for maximizing the Annual Energy Production (AEP), of which design objective should be obtained by FEA (Finite Element Analysis). After all, it is shown that GA combined with MADS has contributed to reducing the computation time effectively for optimal design of PM wind generator when compared with purposely developed GA implemented with the parallel computing method.