[1]高山,单渊达.遗传算法在机组启停中的应用及改进[J].东南大学学报(自然科学版),2000,30(3):51-57.[doi:10.3969/j.issn.1001-0505.2000.03.011]
 Gao Shan,Shan Yuanda.Advanced Genetic Algorithm Approach to Unit Commitment[J].Journal of Southeast University (Natural Science Edition),2000,30(3):51-57.[doi:10.3969/j.issn.1001-0505.2000.03.011]
点击复制

遗传算法在机组启停中的应用及改进()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
30
期数:
2000年第3期
页码:
51-57
栏目:
电气工程
出版日期:
2000-05-20

文章信息/Info

Title:
Advanced Genetic Algorithm Approach to Unit Commitment
作者:
高山 单渊达
东南大学电气工程系,南京 210096
Author(s):
Gao Shan Shan Yuanda
Department of Electrical Engineering, Southeast University, Nanjing 210096
关键词:
机组启停 遗传算法 优化搜索
Keywords:
unit commitment genetic algorithm optimized searching
分类号:
TM731
DOI:
10.3969/j.issn.1001-0505.2000.03.011
摘要:
提出了一种遗传算法应用于机组启停的新思路. 针对机组启停问题的特点,设计了一些启发式技术,使得遗传算法初始种群中的所有个体都是可行解. 针对遗传操作生成的不可行解,建立了一种从不可行域到可行域的映射关系,大大减少了搜索中的无效操作. 对过度满足约束条件的解,提出了一种有效减冗余的手段,并提出了一种边界搜索方法,可以更容易得到更优的解. 这些措施起到了优化搜索路径的作用,有效地提高了遗传算法求解的效率和质量.
Abstract:
This paper discusses an advanced application of genetic algorithm to determine the short-term unit commitment. Some heuristic techniques are developed. First, the method to create initial generation, every unit in the initial generation is a feasible solution of unit commitment. Second, feasibility checking method builds a relation between infeasible solution space and feasible solution space, reduces lots of invalid processes in genetic searching. Third, redundancy-checking method reduces the redundancies of some solutions whose redundancies are too big. Fourth, a boundary searching method can easily get a better solution from a feasible unit. As a result the genetic algorithm used in short-term unit commitment is greatly enhanced.

参考文献/References:

[1] Lee F N.Short-term unit commitment—a new method.IEEE Transaction on Power Apparatus and Systems,1980,99(2):421~428
[2] Snyder W L,Powell H D,Rayburn J C.Dynamic programming approach to unit commitment.IEEE Transactions on Power Systems,1987,2(2):339~350
[3] Dillon T S,Edwin K W,Koche M D,et al.Integer programming approach to the problem of unit commitment with probabilistic reserve determination.IEEE Transaction on Power Apparatus and Systems,1978,97(6):2154~2166
[4] Zhuang F,Galiana F D.Towards a more rigorous and practical unit commitment by lagrangian relaxation.IEEE Transactions on Power Systems,1988,3(2):763~773
[5] 韩学山,柳焯.考虑发电机组输出功率速度限制的最优机组组合.电网技术,1994,18(6):11~15
[6] Dasgupta D,McGregor D R.Thermal unit commitment using genetic algorithms.IEE Proceedings Part-Generation,Transmission and Distribution,1994,141(5):459~465
[7] 韦柳涛,曾庆川,姜铁兵.启发式基因遗传算法及其在电力系统机组组合优化中的应用.中国电机工程学报,1994,11(2):67~72
[8] 蔡超豪,蔡元宇.机组优化组合的遗传算法.电网技术,1997,21(2):44~47
[9] Kazarlis S A,Bakirtzis A G,Petridis V.A genetic algorithm solution to the unit commitment problem.IEEE Transaction on Power Systems,1996,11(1):83~92
[10] Yang H T,Yang P C,Huang C L.A parallel genetic algorithm approach to solving the unit commitment problem:implementation on the transputer networks.IEEE Transactions on Power Systems,1997,12(2):661~668

相似文献/References:

[1]涂青,徐赵东,彭军.隔减震结构中黏弹性阻尼装置的遗传算法优化分析[J].东南大学学报(自然科学版),2009,39(1):73.[doi:10.3969/j.issn.1001-0505.2009.01.014]
 Tu Qing,Xu Zhaodong,Peng Jun.Parametric optimization of viscoelastic device in earthquake isolation and mitigation of structures[J].Journal of Southeast University (Natural Science Edition),2009,39(3):73.[doi:10.3969/j.issn.1001-0505.2009.01.014]
[2]黄裕洋,金远平.一种基于余弦因子改进的混合聚类算法[J].东南大学学报(自然科学版),2010,40(3):496.[doi:10.3969/j.issn.1001-0505.2010.03.012]
 Huang Yuyang,Jin Yuanping.Hybrid clustering algorithm based on cosine factor improvement[J].Journal of Southeast University (Natural Science Edition),2010,40(3):496.[doi:10.3969/j.issn.1001-0505.2010.03.012]
[3]周建新,司风琪,仇晓智,等.基于SVR和GA的锅炉运行氧量基准值的优化确定[J].东南大学学报(自然科学版),2008,38(6):1061.[doi:10.3969/j.issn.1001-0505.2008.06.024]
 Zhou Jianxin,Si Fengqi,Qiu Xiaozhi,et al.Optimization of boiler operation oxygen content based on support vector regression and genetic algorithms[J].Journal of Southeast University (Natural Science Edition),2008,38(3):1061.[doi:10.3969/j.issn.1001-0505.2008.06.024]
[4]曹源,汪凤泉,桂益俊.基于遗传算法的冲击信号拟合[J].东南大学学报(自然科学版),2007,37(2):320.[doi:10.3969/j.issn.1001-0505.2007.02.027]
 Cao Yuan,Wang Fengquan,Gui Yijun.Research of shock signal curve fit based on genetic algorithm[J].Journal of Southeast University (Natural Science Edition),2007,37(3):320.[doi:10.3969/j.issn.1001-0505.2007.02.027]
[5]刘宁,刘怀,费树岷.网络控制系统中任务与信息的优化调度[J].东南大学学报(自然科学版),2007,37(4):605.[doi:10.3969/j.issn.1001-0505.2007.04.012]
 Liu Ning,Liu Huai,Fei Shumin.Optimal task and message scheduling for networked control systems[J].Journal of Southeast University (Natural Science Edition),2007,37(3):605.[doi:10.3969/j.issn.1001-0505.2007.04.012]
[6]洪伟.计算电磁学研究进展[J].东南大学学报(自然科学版),2002,32(3):335.[doi:10.3969/j.issn.1001-0505.2002.03.006]
 Hong Wei.Progress in computational electromagnetics[J].Journal of Southeast University (Natural Science Edition),2002,32(3):335.[doi:10.3969/j.issn.1001-0505.2002.03.006]
[7]叶在福,单渊达.计及非定量不确定性的多种群遗传电网扩展规划[J].东南大学学报(自然科学版),2000,30(2):116.[doi:10.3969/j.issn.1001-0505.2000.02.025]
 Ye Zaifu,Shan Yuanda.A New Transmission Network Expansion Planning Using Improved Multiple-Population Genetic Algorithm[J].Journal of Southeast University (Natural Science Edition),2000,30(3):116.[doi:10.3969/j.issn.1001-0505.2000.02.025]
[8]谈烨,仲伟俊,徐南荣.基于遗传算法的一类资源分配两层规划问题求解[J].东南大学学报(自然科学版),1999,29(4):12.[doi:10.3969/j.issn.1001-0505.1999.04.003]
 Tan Ye,Zhong Weijun,Xu Nanrong.A Genetic Algorithm Based Method for a Class of Resource Allocation Bilevel Programming Problems[J].Journal of Southeast University (Natural Science Edition),1999,29(3):12.[doi:10.3969/j.issn.1001-0505.1999.04.003]
[9]徐欧,杨非,孙忠良.亚毫米波对角喇叭天线的GA优化设计及其性能测试[J].东南大学学报(自然科学版),2010,40(6):1134.[doi:10.3969/j.issn.1001-0505.2010.06.002]
 Xu Ou,Yang Fei,Sun Zhongliang.Genetic algorithm design and measurement of sub-millimeter wave diagonal horn[J].Journal of Southeast University (Natural Science Edition),2010,40(3):1134.[doi:10.3969/j.issn.1001-0505.2010.06.002]
[10]张杰,马浩,吴镇扬.基于遗传算法的零极点型与头相关传递函数优化逼近[J].东南大学学报(自然科学版),2006,36(1):19.[doi:10.3969/j.issn.1001-0505.2006.01.004]
 Zhang Jie,Ma Hao,Wu Zhenyang.Optimal approximation of head-related transfer function’s zero-pole model based on genetic algorithm[J].Journal of Southeast University (Natural Science Edition),2006,36(3):19.[doi:10.3969/j.issn.1001-0505.2006.01.004]

备注/Memo

备注/Memo:
第一作者:男, 1973年生, 博士研究生.
更新日期/Last Update: 2000-05-20