[1]彭道刚,赵寒梅,黄 丽,等.基于混沌粒子群优化的火电机组负荷分配[J].南京理工大学学报(自然科学版),2017,41(04):526.[doi:10.14177/j.cnki.32-1397n.2017.41.04.019]
 Peng Daogang,Zhao Hanmei,Huang Li,et al.Load distribution in power plants based onchaotic-particle swarm optimization[J].Journal of Nanjing University of Science and Technology,2017,41(04):526.[doi:10.14177/j.cnki.32-1397n.2017.41.04.019]
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基于混沌粒子群优化的火电机组负荷分配()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
41卷
期数:
2017年04期
页码:
526
栏目:
出版日期:
2017-08-31

文章信息/Info

Title:
Load distribution in power plants based onchaotic-particle swarm optimization
文章编号:
1005-9830(2017)04-0526-07
作者:
彭道刚1赵寒梅1黄 丽1顾立群2王维建3
1.上海电力学院 自动化工程学院 上海发电过程智能管控工程技术研究中心,上海200090; 2.宝山钢铁股份有限公司电厂,上海201900; 3.上海新华控制技术(集团)有限公司,上海200241
Author(s):
Peng Daogang1Zhao Hanmei1Huang Li1Gu Liqun2Wang Weijian3
1.School of Automation Engineering,Shanghai University of Electric Power,Shanghai Engineering ResearchCenter of Intelligent Management and Control for Power Process,Shanghai 200090,China; 2.Power Plant of Baoshan Iron & Steel Co.Ltd.,Shanghai 201900
关键词:
负荷优化分配 煤耗特性 阀点效应 混沌粒子群算法 动态惯性权重
Keywords:
optimal load distribution coal consumption characteristics valve-point effect chaotic-particle swarm optimization dynamic weight
分类号:
TP273
DOI:
10.14177/j.cnki.32-1397n.2017.41.04.019
摘要:
为进一步优化电厂负荷的分配,有效提高发电厂的经济效益,在粒子群算法的基础上结合混沌优化等算法对电厂负荷的分配进行了改进研究。该方法从火电厂的实际发电情况出发,考虑了阀点效应和机组在实际运行过程中的约束条件,建立了发电机组负荷优化分配模型,并利用罚函数法将此模型转化为非约束问题求解,实现了利用混沌粒子群算法来优化发电机组负荷,解决了局部最优和收敛速度慢等问题。通过实例分析证明了该算法的可行性。
Abstract:
To optimize the load distribution further and improve the economic efficiency of power plant,this paper improves the load distribution of the power plant based on the particle swarm optimization algorithm and chaotic optimization algorithm.Considering the actual situation of the power generation power plant,this method establishes the optimal load distribution model of generator,which takes the constraints of the valve point effect and units in the actual operation process into consideration.By using the penalty function method,this model is transformed into a non constrained problem.A practical example analysis has verified that the improved chaotic-particle swarm optimization algorithm is feasible in solving the problem of optimal load distribution,partial optimum and slowness of convergence rate.

参考文献/References:

[1] 王慧杰,范志愿,李鑫鑫.基于线性规划法和等微增率法的电厂负荷优化分配[J].电力科学与工程,2016,32(1):1-5.
Wang Huijie,Fan Zhiyuan,Li Xinxin.Optimizational method based on the linear programming method and the equal incremental principle of load distribution in power plant[J].Electric Power Science and Engineering,2016,32(1):1-5.
[2]向德军,陈根军,顾全,等.基于实测煤耗的AGC电厂负荷优化分配[J].电力系统自动化,2013,37(17):125-128.
Xiang Dejun,Chen Genjun,Gu Quan,et al.Optimal load distribution for AGC power plant based on real-time coal consumption[J].Automation of Electric Power Systems,2013,37(17):125-128.
[3]王宁玲,付鹏,陈德刚,等.大数据分析方法在厂级负荷分配中的应用[J].中国电机工程学报,2015,35(1):68-73.
Wang Ningling,Fu Peng,Chen Degang,et al.Application of big data analytics in plant-level load dispatching of power plant[J].Proceedings of the CSEE,2015,35(1):68-73.
[4]Abido M A.Multiobjective evolutionary algorithms for electric power dispatch problem[J].IEEE Transactions on Evolutionary Computation,2006,10(3):315-329.
[5]赵莉.基于改进量子粒子群算法的云计算资源调度[J].南京理工大学学报,2016,40(2):223-228.
Zhao Li.Cloud computing resource scheduling based on improved quantum partical swarm optimization algorithm[J].Journal of Nanjing University of Science and Technology,2016,40(2):223-228.
[6]亢国栋,孙伟,杨海群,等.基于改进粒子群优化算法的火电厂机组负荷分配[J].计算机测量与控制,2015,23(2):593-596.
Kang Guodong,Sun Wei,Yang Haiqun,et al.Unit load economic dispatch of power plant based on improved particale swarm optimization alogorithm[J].Computre Measurement & Control,2015,23(2):593-596.
[7]陈志敏,薄煜明,吴盘龙,等.收敛粒子群全区域自适应粒子滤波算法及其应用[J].南京理工大学学报,2012,36(5):861-868.
Chen Zhimin,Bo Yuming,Wu Panlong,et al.Novel lanscape addptive partical filter algorithm based on convergent partical swarm and its application[J].Journal of Nanjing University of Science and Technology,2012,36(5):861-868.
[8]Zhao B,Guo C X,Bai B R,et al.An improved particle swarm optimization algorithm for unit commitment[J].Applied Mathematics and Computation,2007,193(1):231-239.
[9]Wang Lin,Yang Bo,Chen Yuehui.Improving particle swarm optimizatiion using multi-layer searching strategy[J].Information Sciences,2014,274(8):70-94.
[10]吴辰斌,李海明,刘栋,等.一种改进型粒子群优化算法在电力系统经济负荷分配中的应用[J].电力系统保护与控制,2016,44(10):44-48.
Wu Chenbin,Li Haiming,Liu Dong,et al.Application of improved particle swarm optimization algorithm to power system economic load dispatch[J].Power System Protection and Control,2016,44(10):44-48.
[11]韩朝兵,吕晓明,司风琪,等.基于改进混沌粒子群算法的火电厂经济负荷分配[J].动力工程学报,2015,35(4):312-317.
Han Chaobing,Lv Xiaoming,Si Fengqi,et al.Study on economic load dispatch based on improved CPSO algorithm for thermal power plants[J].Journal of Chinese Society of Power Engineering,2015,35(4):312-317.
[12]司风琪,顾慧,叶亚兰,等.基于混沌粒子群算法的火电厂厂级负荷在线优化分配[J].中国电机工程学报,2011,26(31):103-109.
Si Fengqi,Gu Hui,Ye Yalan,et al.Online unit load economic dispatch based on chaotic-particle swarm optimization algorithm[J].Proceedings of the CSEE,2011,26(31):103-109.

备注/Memo

备注/Memo:
收稿日期:2016-09-21 修回日期:2016-11-23基金项目:上海市“科技创新行动计划”高新技术领域项目(16111106300; 17511109400); 上海市科学技术委员会工程技术研究中心项目(14DZ2251100); 上海市“科技创新行动计划”社会发展领域项目(16DZ1202500)
作者简介:彭道刚(1977-),男,博士,教授,主要研究方向:发电过程自动化、负荷调度优化、能源互联网等,E-mail:pengdaogang@126.com。
引文格式:彭道刚,赵寒梅,黄丽,等.基于混沌粒子群优化的火电机组负荷分配[J].南京理工大学学报,2017,41(4):526-532.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-08-31