文化微粒群算法及其在风电场风能资源评估中的应用
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摘要
伴随着全球范围内的常规能源储量下降以及环境污染日趋严重等问题的影响,各国都认识到了开发新能源迫在眉睫。风能因其无污染,利用率高,具有大规模开发利用的前景受到越来越广泛的关注。近年来,我国也很重视风能资源开发,已建和在建有多个大规模风电场。因此,对于拟建风场来说,风能资源评估和风机选型具有重要的现实意义。本文的主要研究内容是智能优化算法在风电场风能资源评估和风电场风机优化选型中的应用。
本文在对文化算法和微粒群算法进行详细分析后,提出了一种增强型的文化微粒群算法,将微粒群算法应用于文化算法框架的主群体空间,改进了微粒群算法的速度更新公式,同时对信念空间的知识及其更新方式进行了重新定义,提出了用历史知识来引导形势知识更新的策略,从而使得这种混合算法在寻优过程中既可以保持种群多样性,也具有较快的收敛速度。实验结果证明,该算法是一种能够发挥文化算法优点,提高微粒群算法性能的混合算法。
在提出以上改进混合算法后,本文以风能资源评估中的Weibull分布参数为研究对象,将增强型文化微粒群算法应用于Weibull分布参数的优化,优化得到的参数值精度较高,由该参数拟合的风速分布曲线接近实际曲线,这既体现了算法的实用性,也再次验证了算法的优化性能。
最后,针对风电场风机优化选型的需要,建立最大装机容量模型和最优性价比模型两个风机优化选型模型,并将改进的离散文化微粒群算法应用到风电场风机选型的模型的优化中,为拟建风场确定所选风机类型和数量提供依据,在实际算例中取得较好的效果。
Facing in such problems as global decline of reservation of conventional energy and poor environmental conditions, many countries recognize that it is in emergency that developing a new technique to use the new energy sufficiently. Wind energy is given close attention because of its characteristic of non-polluting, high utilization and with the prospect of large-scale development and utilization. In recent years, China also attaches great importance to the development of wind energy resources. Many large-scale wind farms have been built or are in building. Therefore, the method of the wind resource assessment and wind turbine selection has important impact on the wind farm. The primary research content of this paper is the application of intelligent optimization algorithm in wind energy assessment and optimal selection of wind turbine.
After analysis of Cultural Algorithm (CA) and Particle Swarm Optimization (PSO) in details, an Enhanced Culture-Based Particle Swarm Optimization algorithm (ECPSO) has been proposed. In this mixed algorithm, PSO algorithm is used in the Population Space of CA frame and the update formula of the velocity of PSO algorithm is modified. Then this paper redesigns the knowledge of Believe Space and its update method, and a strategy is proposed which makes the history knowledge guide the update of the situational knowledge. The purpose of all the modify methods in this algorithm is to remain the diversity of the population as well as having a faster convergence velocity. The experimental result indicates that this mixed algorithm can show the effect of culture frame enough and enhance the capability of PSO algorithm efficiently.
Proposing the hybrid algorithm, this paper chooses Weibull distribution parameters for the study. ECPSO algorithm is applied to optimizing the two parameters of Weibull distribution. The parameters optimized by ECPSO can obtain high precision, and the wind speed distribution curve fitted by the parameters is close to the actual curve. This not onlv reflects the practicality of the algorithm, but also reminds the optimal performance of the algorithm.
Finally, this paper builds the largest capacity model and the highest performance cost rate model to meet the demand of selection of wind farm wind turbine. Enhanced Culture-Based Distributed Particle Swarm Optimization Algorithm (ECDPSO) is applied to optimizing the selection optimization model of wind turbine, to determining the type and number of wind turbine for the proposed wind farm. So ECDPSO has a good performance in the practical example.
本文在对文化算法和微粒群算法进行详细分析后,提出了一种增强型的文化微粒群算法,将微粒群算法应用于文化算法框架的主群体空间,改进了微粒群算法的速度更新公式,同时对信念空间的知识及其更新方式进行了重新定义,提出了用历史知识来引导形势知识更新的策略,从而使得这种混合算法在寻优过程中既可以保持种群多样性,也具有较快的收敛速度。实验结果证明,该算法是一种能够发挥文化算法优点,提高微粒群算法性能的混合算法。
在提出以上改进混合算法后,本文以风能资源评估中的Weibull分布参数为研究对象,将增强型文化微粒群算法应用于Weibull分布参数的优化,优化得到的参数值精度较高,由该参数拟合的风速分布曲线接近实际曲线,这既体现了算法的实用性,也再次验证了算法的优化性能。
最后,针对风电场风机优化选型的需要,建立最大装机容量模型和最优性价比模型两个风机优化选型模型,并将改进的离散文化微粒群算法应用到风电场风机选型的模型的优化中,为拟建风场确定所选风机类型和数量提供依据,在实际算例中取得较好的效果。
Facing in such problems as global decline of reservation of conventional energy and poor environmental conditions, many countries recognize that it is in emergency that developing a new technique to use the new energy sufficiently. Wind energy is given close attention because of its characteristic of non-polluting, high utilization and with the prospect of large-scale development and utilization. In recent years, China also attaches great importance to the development of wind energy resources. Many large-scale wind farms have been built or are in building. Therefore, the method of the wind resource assessment and wind turbine selection has important impact on the wind farm. The primary research content of this paper is the application of intelligent optimization algorithm in wind energy assessment and optimal selection of wind turbine.
After analysis of Cultural Algorithm (CA) and Particle Swarm Optimization (PSO) in details, an Enhanced Culture-Based Particle Swarm Optimization algorithm (ECPSO) has been proposed. In this mixed algorithm, PSO algorithm is used in the Population Space of CA frame and the update formula of the velocity of PSO algorithm is modified. Then this paper redesigns the knowledge of Believe Space and its update method, and a strategy is proposed which makes the history knowledge guide the update of the situational knowledge. The purpose of all the modify methods in this algorithm is to remain the diversity of the population as well as having a faster convergence velocity. The experimental result indicates that this mixed algorithm can show the effect of culture frame enough and enhance the capability of PSO algorithm efficiently.
Proposing the hybrid algorithm, this paper chooses Weibull distribution parameters for the study. ECPSO algorithm is applied to optimizing the two parameters of Weibull distribution. The parameters optimized by ECPSO can obtain high precision, and the wind speed distribution curve fitted by the parameters is close to the actual curve. This not onlv reflects the practicality of the algorithm, but also reminds the optimal performance of the algorithm.
Finally, this paper builds the largest capacity model and the highest performance cost rate model to meet the demand of selection of wind farm wind turbine. Enhanced Culture-Based Distributed Particle Swarm Optimization Algorithm (ECDPSO) is applied to optimizing the selection optimization model of wind turbine, to determining the type and number of wind turbine for the proposed wind farm. So ECDPSO has a good performance in the practical example.
引文
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[3]Kennedy J, Eberhart R C, Shi Y. Swarm Intelligence[M]. Morgan Kaufmann Publishers. 2001
[4]吴斌.群体智能的研究及其在知识发现中的应用.中国科学院计算技术研究所博士学位论文[D].2002：1-3,14-16
[5]Colorni A, Dorigo M, Maniezzo V.Distributed Optimization by Ant Colonies[C]. Proceedings of The 1st European Conference On Artificial Life,1991:134-143
[6]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式：鱼群算法[J].系统工程理论与实践.2002,22(11)：32-38
[7]陈国初,俞金寿.微粒群优化算法[J].信息与控制,2005,34(3)：318-324.
[8]Peng B. Knowledge and Population Swarms in Cultural Algorithms for Dynamic Environments[D]. USA:Wayne State University,2005
[9]Chen Shuyong, Dai Huizhu, Bai Xiaomin et al. Reliability Model of Wind Power Plants and Its Application[J]. Proceedings of the CSEE,2000,3(3):26-29
[10]雷亚洲,Gordon Lightbody.国外风力发电导则及动态模型简介[J].电网技术,2005,25(12)：27-32
[11]尹炼.风力发电[M].北京：中国水利水电出版社,2002
[12]Cellura M., Cirrincione G., Marvuglia A., et al. Wind Speed Spatial Estimation for Energy Planning in Sicily:Introduction and Statistical Analysis [J]. Renewable Energy 2008,33(6):1239-1240
[13]李渝,范高锋,李庆,赵海翔,戴慧珠.达坂城风电接入系统对新疆电网电能质量的影响[J].电网技术,31(6)：88-92
[14]Eberhart R.C., Kenney J. Particle Swarm Optimization[C]. In Proceedings of the Fourth IEEE International Conference on Neural Networks.1995:1948-1951
[15]Eberhart R.C., Kenney J. A New Optimizer Using Particle Swarm Theory[C]. Proceeding of the sixth international symposium on micro machine and human science. 1995:39-43
[16]王凌.智能优化算法及其应用[M].北京.清华大学出版社.2001
[17]Shi Y, Eberhart R.C.. A modified particle swarm optimizer[C]. Proc. Of the IEEE CEC, 1998:69-73.
[18]Shi Y, Eberhart R.C.. Fuzzy adaptive particle swarm optimization[C]. Proc. Of the IEEE CEC.2001:101-106
[19]陈国初,俞金寿.增强型微粒群优化算法及其在软测量中的应用[J].控制与决策,2005,20(4)：377-381
[20]Parsopoulos K E, Plagianakos V P, Magoulas G D, Vrahatis M.N. Stretching technique for obtaining global minimizers through particle swarm optimization[C]. Proceedings of the Particle Swarm Optimization workshop,2001:22-29
[21]Shi Y, Eberhart R C.A modified particle swarm optimizer[C]. Proc. Of the IEEE CEC,1998:69-73
[22]Clerc M, Kennedy J. The particle swarm:explosion, stability, and convergence in a multi-dimension complex space[J]. IEEE Trans. Evolution. Computer,2002,6:58-73
[23]钟一文.智能优化方法及其应用研究[D].浙江大学博士论文,2005
[24]李宁,邹彤,孙德宝.带时间窗车辆路径问题的粒子群算法[J].系统工程理论与实践,2004,24(4)：131-135
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