Selection of Ocean Current Turbine Parameters and Optimal Allocation of Current Turbines in Large Ocean Current Farms

碩士 === 清雲科技大學 === 電機工程研究所 === 96 === At present, the development of ocean current generator technology is at the stage of single-machine prototype design or test. The generation amount of each generator is small. Hence, the ocean current farm will be created by aggregating many ocean current generat...

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Main Authors: Ching-Hsin Liao, 廖經欣
Other Authors: Hou-Sheng Huang
Format: Others
Language:zh-TW
Published: 2008
Online Access:http://ndltd.ncl.edu.tw/handle/34004763683657437520
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spelling ndltd-TW-096CYU004420092016-05-16T04:10:16Z http://ndltd.ncl.edu.tw/handle/34004763683657437520 Selection of Ocean Current Turbine Parameters and Optimal Allocation of Current Turbines in Large Ocean Current Farms 海流發電機參數選擇及大型海流場中海流發電機配置最佳化之探討 Ching-Hsin Liao 廖經欣 碩士 清雲科技大學 電機工程研究所 96 At present, the development of ocean current generator technology is at the stage of single-machine prototype design or test. The generation amount of each generator is small. Hence, the ocean current farm will be created by aggregating many ocean current generators in the future. There are two purposes in this thesis. One is, according to the distribution characteristics of ocean currents, to obtain the optimal speed parameters, such as cut-in speed and rated speed, of single generator by the Turbine Performance Index (TPI). Another is, in the ocean current farm with fixed area, to obtain the optimal number and locations of the generators under the considerations of wake effect, generator model and cost model. It can be used to achieve the best profit of the ocean current farm. In the study of the first purpose, the speed distributions of Taiwan's offshore ocean currents are guaranteed as normal distribution and the closed form equation of TPI is conducted. The average current speeds of Taiwan's offshore ocean currents are between 0.26 m/s and 1.17 m/s. The deeper the depths are, the slower the average speeds will be. The optimal cut-in speeds are under 0.7 m/s and optimal rated speeds are between about 1.04m/s and 2.24 m/s. In the study of the second purpose, Simple Genetic Algorithm (SGA), Hybrid Distributed Genetic Algorithm (HDGA), Binary Particle Swarm Optimization (BPSO) and Hybrid Binary Particle Swarm Optimization (HBPSO) are used to obtain the optimal solutions of number and locations of the ocean current generators. Test results show that BPSO and HBPSO are better than that of SGA and HDGA in performance. Where the BPSO can obtain the best accurate solution and executing speed is quite fast. However, if the faster speed is desirable, the HPBSO can be suggested, but accuracy must be degraded a little. Hou-Sheng Huang 黃厚生 2008 學位論文 ; thesis 106 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 碩士 === 清雲科技大學 === 電機工程研究所 === 96 === At present, the development of ocean current generator technology is at the stage of single-machine prototype design or test. The generation amount of each generator is small. Hence, the ocean current farm will be created by aggregating many ocean current generators in the future. There are two purposes in this thesis. One is, according to the distribution characteristics of ocean currents, to obtain the optimal speed parameters, such as cut-in speed and rated speed, of single generator by the Turbine Performance Index (TPI). Another is, in the ocean current farm with fixed area, to obtain the optimal number and locations of the generators under the considerations of wake effect, generator model and cost model. It can be used to achieve the best profit of the ocean current farm. In the study of the first purpose, the speed distributions of Taiwan's offshore ocean currents are guaranteed as normal distribution and the closed form equation of TPI is conducted. The average current speeds of Taiwan's offshore ocean currents are between 0.26 m/s and 1.17 m/s. The deeper the depths are, the slower the average speeds will be. The optimal cut-in speeds are under 0.7 m/s and optimal rated speeds are between about 1.04m/s and 2.24 m/s. In the study of the second purpose, Simple Genetic Algorithm (SGA), Hybrid Distributed Genetic Algorithm (HDGA), Binary Particle Swarm Optimization (BPSO) and Hybrid Binary Particle Swarm Optimization (HBPSO) are used to obtain the optimal solutions of number and locations of the ocean current generators. Test results show that BPSO and HBPSO are better than that of SGA and HDGA in performance. Where the BPSO can obtain the best accurate solution and executing speed is quite fast. However, if the faster speed is desirable, the HPBSO can be suggested, but accuracy must be degraded a little.
author2 Hou-Sheng Huang
author_facet Hou-Sheng Huang
Ching-Hsin Liao
廖經欣
author Ching-Hsin Liao
廖經欣
spellingShingle Ching-Hsin Liao
廖經欣
Selection of Ocean Current Turbine Parameters and Optimal Allocation of Current Turbines in Large Ocean Current Farms
author_sort Ching-Hsin Liao
title Selection of Ocean Current Turbine Parameters and Optimal Allocation of Current Turbines in Large Ocean Current Farms
title_short Selection of Ocean Current Turbine Parameters and Optimal Allocation of Current Turbines in Large Ocean Current Farms
title_full Selection of Ocean Current Turbine Parameters and Optimal Allocation of Current Turbines in Large Ocean Current Farms
title_fullStr Selection of Ocean Current Turbine Parameters and Optimal Allocation of Current Turbines in Large Ocean Current Farms
title_full_unstemmed Selection of Ocean Current Turbine Parameters and Optimal Allocation of Current Turbines in Large Ocean Current Farms
title_sort selection of ocean current turbine parameters and optimal allocation of current turbines in large ocean current farms
publishDate 2008
url http://ndltd.ncl.edu.tw/handle/34004763683657437520
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