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浙江大学学报(工学版)
J4  2013, Vol. 47 Issue (12): 2087-2093    DOI: 10.3785/j.issn.1008-973X.2013.12.003
电气工程     
基于粒子群算法的潮流发电机布局
刘丞1, 汪昆2, 汪雄海1
1.浙江大学 电气工程学院,浙江 杭州 310027；2.浙江省杭州市电力局,浙江 杭州 310009
Optimal deployment of tidal current turbines based on particle swarm algorithm
LIU Cheng1, WANG Kun2, WANG Xiong-hai1
1. College of Electric Engineering, Zhejiang University, Hangzhou 310027, China； 2. Hangzhou Municipal Electric Power Bureau, Hangzhou 310009, China
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摘要：

针对任何给定背景条件下的潮流能发电场的开发,都存在装置布局及潮流机尾流影响发电量的问题.提出一种改进的自适应罚函数粒子群新优化算法,以解决潮流发电机；布局优化及已知海域潮流能合理开发难题.先由给定海域单位发电量成本最小为目标求取该潮流电场最佳装机数,再依据设备在给定的任一时间段内总发电量与潮流机位置坐标的关系求出各潮流机最优布局方案,论证了最优布局方案能减小尾流影响、可明显提高潮流能利用效率.详细推导了算法模型、布局优化策略.结果表明：新优化算法理论分析及推理正确；可行解搜索及相同台数潮流能发电机布局均达到了较好的效果；潮流机在各种入流速度下的发电量比传统算法布局方案有明显提高.
Abstract:

For tidal power generation, both the layout scheme of tidal current turbines and the turbine’s wake influence the power generation efficiency. An improved particle swarm optimization algorithm based on an adaptive penalty function was proposed to solve the problem of optimal deployment of tidal current turbines. First, the optimal number of tidal current turbines in a given tidal farm was obtained by minimizing the cost of unit power generation. Then, an optimal layout scheme was developed in terms of the relation between the total generation power over a period of time and the layout of turbines. It was shown that the proposed optimal deployment scheme apparently decreases the wake effect and increases the efficiency of tidal power generation. The detailed derivation of the optimal layout strategy was also included. Finally, a simulation result was provided, which validates the proposed scheme and shows the improvement of power generation efficiency comparing with several known traditional schemes.
出版日期: 2013-12-01
:  TP 273.1  
基金资助:

国家“973”重点基础研究发展规划资助项目(2009CB320602).
通讯作者: 汪雄海,男,教授,博导.     E-mail: wxh_10@zju.edu.cn
作者简介: 刘丞(1988—),男,硕士生,主要从事智能控制算法与应用技术研究.E-mail: lc8517335@zju.edu.cn
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引用本文:

刘丞, 汪昆, 汪雄海. 基于粒子群算法的潮流发电机布局[J]. J4, 2013, 47(12): 2087-2093.

LIU Cheng, WANG Kun, WANG Xiong-hai. Optimal deployment of tidal current turbines based on particle swarm algorithm. J4, 2013, 47(12): 2087-2093.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2013.12.003    或    http://www.zjujournals.com/eng/CN/Y2013/V47/I12/2087

［1］ BLUNDEN L S, BAHAJ A S. Tidal energy resource assessment for tidal stream generators［J］. Proceedings of Institution of Mechanical Engineers: Part A Journal of Power Energy, 2007, 221(A2): 137-146.

［2］ MYERS L, BAHAJ A S. Simulated electrical power pote-ntial harnessed by marine current turbine arrays in the Alderney Race［J］. Renewable Energy,2005, 30(11): 1713-1731.

［3］ SEUNG H L, SANG H L, KYUNGSOO J. A numerical study for the optimal arrangement of ocean current turbine generators in the ocean current power parks［C］∥ International Renewable Energy Confe-rence and Exhibition. Busan: ELSEVIER, 2010: S137-S141.

［4］ PALM M, HUIJSMANS R, POURQUIE M. Simple wake models for tidal turbines in farm arrangement［C］∥ 29th ASME International Conference on Ocean, Offshore an-d Arctic Engineering. Shanghai: ［s.n.］, 2010, 3: 577-587.

［5］ GRADY S A, HUSSAINI M Y, ABDULLAH M M. Placement of wind turbines using genetic algorithms［J］. Renewable Energy, 2005, 30(2): 259-270.

［6］ ZHANG Chang-shui, HOU Guang-dong, WANG Jun. A fast algorithm based on the submodular property for optimization of wind turbine positioning［J］. Renewable Energy, 2011, 36(11): 2951-2958.

［7］ KUSIAK A, SONG Z. Design of wind farm layout for maximum wind energy capture［J］. Renewable Energy, 2010, 35(3): 685-694.

［8］ EROGLU Y, SECKINER S U. Design of wind farm layout using ant colony algorithm［J］. Renewable Energy, 2012, 44: 53-62.

［9］ TESSEMA B, YEN G G. A self adaptive penalty f-unction based algorithm for constrained optimization［C］∥ IEEE Congress on Evolutionary Computation. Vancouver: IEEE, 2006: 246-253.

［10］ WANG Yong, CAI Zi-xing, ZHOU Yu-ren. An adaptive tradeoff model for constrained evolutionary optimization［J］. IEEE Transactions On Evolutionary Computation, 2008, 12(1): 80-92.

［11］ KENNEDY J, EBERHART R C. Particle swarm optimization［C］∥ Proceedings of the 1995 IEEE International Conference on Neural Networks-Part 4(of 6). Piscataway: IEEE, 1995: 1942-1948.

［12］ CLERC M. The swarm and the queen: Towards a deterministic and adaptive particle swarm optimizati-on［C］∥ Proceedings of the Congress of Evolution-ary Computation. Washington:［s.n.］, 1999: 1951-1957.

［13］ JENSEN N O. A note on wind turbine interaction［R］. Roskilde Denmark: Riso National Laboratory, 1983.

［14］ KIRANOUDIS C T, MAROULIS Z B. Effective sh-ort-cut modelling of wind park efficiency［J］. Renewable Energy, 1997, 11(4): 439-457.

［15］ BEDARD R. Survey and characterization tidal in st-ream energy conversion (TISEC) devices［R］. California: EPRI, 2005.
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