基于量子遗传算法的电力系统无功优化
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摘要
随着国民经济的快速发展,各个行业对电能质量的要求不断提高。电力系统无功优化是保证系统安全、经济运行的一项有效手段,是降低网损、提高电压质量的重要措施。因此,电力系统无功优化问题的研究,既具有理论意义,又具有实际应用价值。
电力系统的无功优化问题是一个多目标、多变量、多约束的混合非线性规划问题,其操作变量既有连续变量又有离散变量,使得优化过程十分复杂。本文介绍了电力系统无功优化领域的研究现状及其发展,概述了求解无功优化问题的方法,这些方法可以大致分成两类:经典方法和现代方法。经典方法主要指确定性搜索方法,现代方法包括人工智能方法(尤其是遗传算法)、禁忌搜索算法和模拟退火算法等。
但这些方法都不同程度的存在一些缺点及它的局限性,因此本文深入地研究了无功优化的特点,建立了以有功网损为目标函数的电力系统无功优化数学模型,提出了一种应用于电力系统无功优化的新算法—量子遗传算法。量子遗传算法是将量子计算与遗传算法相结合的一种崭新的优化方法,用量子比特为基本信息位编码染色体,用基于量子概率门的量子变异实现个体进化,具有种群规模小、收敛速度较快,全局寻优能力强的特点,有很大的生命力和研究价值,能大大提高遗传算法的效率,弥补遗传算法的不足。
将量子遗传算法应用于电力系统的无功优化问题中,通过MATLAB编制程序对IEEE-14和IEEE-30标准系统的测试表明,本文所提出的基于量子遗传算法的电力系统无功优化方法是正确有效的,比传统遗传算法收敛速度快、全局寻优能力强。
同时,考虑到无功优化运行问题中电压稳定性的影响,在传统无功优化模型中引入静态电压稳定指标,建立了以网损最小、静态电压稳定裕度最大为目标的多目标无功优化模型,并将该算法用于IEEE-14系统,结果验证了模型和算法的有效性。
With the development of national economy, the demands of power supply quality from all kinds of industries are increased. Reactive power optimization is one of the most important control methods to ensure power system operation securely and economically, and an effective measure to improve the voltage profile and reduce the transmission loss. Study the problem of reactive power optimization has the great significance in theory and practical application.
Reactive power optimization is a large-scale nonlinear optimization problem with a large number of objects, variables and uncertain parameters, the operating variables include continuous and discrete variables, so the optimization becomes very complex. This paper introduces the process of development and research actuality of reactive power optimization in power system, summarizes the methods of reactive power optimization; The methods mainly include two kinds: sutra and modern. The sutra method mainly refers to definitive searchable method, the modern method involve the manpower intelligence method (especially the genetic algorithm), the tabu searchable method and the simulated anneal etc.
But the methods have some defects and limitations in certain degree, this paper discusses the characteristics of reactive power optimization thorough, establishes the basic mathematical model and the object function of model is based on active power loss, and puts forward a novel algorithm-quantum genetic algorithm (QGA) which is applied to the problem of reactive power optimization of power system. Quantum Genetic Algorithm is a new optimum method that combines quantum computation with Genetic Algorithms. It uses quantum bit as coding which is different from conventional binary coding completely. QGA makes full use of superposition of quantum states to keep diversity of population and avoid prematurity of population. So it has good characteristics of strong search capability, rapid convergence and no premature. It appears strong life-force and valuable for research. It can greatly improve the computation efficiency of GA and remedy shortcoming.
QGA is applied to the problem of reactive power optimization of power system, the computing results against the IEEE-14 and IEEE-30 criterion testing system prove that method of reactive power optimization based on QGA proposed in this paper is effective. Compared with the traditional genetic algorithm, QGA has good characteristics of strong search capability, rapid convergence and no premature, is superior to conventional genetic algorithms in quality and efficiency.
This paper incorporates the voltage stability view to reactive power dispatch and control problem, a model of multi-objective reactive power optimization is established, which takes into account of loss minimization, voltage stability margin maximization and high service quality. The simulations are carried out on IEEE-14 bus system, and the results show the validity of the proposed model and algorithm.
电力系统的无功优化问题是一个多目标、多变量、多约束的混合非线性规划问题,其操作变量既有连续变量又有离散变量,使得优化过程十分复杂。本文介绍了电力系统无功优化领域的研究现状及其发展,概述了求解无功优化问题的方法,这些方法可以大致分成两类:经典方法和现代方法。经典方法主要指确定性搜索方法,现代方法包括人工智能方法(尤其是遗传算法)、禁忌搜索算法和模拟退火算法等。
但这些方法都不同程度的存在一些缺点及它的局限性,因此本文深入地研究了无功优化的特点,建立了以有功网损为目标函数的电力系统无功优化数学模型,提出了一种应用于电力系统无功优化的新算法—量子遗传算法。量子遗传算法是将量子计算与遗传算法相结合的一种崭新的优化方法,用量子比特为基本信息位编码染色体,用基于量子概率门的量子变异实现个体进化,具有种群规模小、收敛速度较快,全局寻优能力强的特点,有很大的生命力和研究价值,能大大提高遗传算法的效率,弥补遗传算法的不足。
将量子遗传算法应用于电力系统的无功优化问题中,通过MATLAB编制程序对IEEE-14和IEEE-30标准系统的测试表明,本文所提出的基于量子遗传算法的电力系统无功优化方法是正确有效的,比传统遗传算法收敛速度快、全局寻优能力强。
同时,考虑到无功优化运行问题中电压稳定性的影响,在传统无功优化模型中引入静态电压稳定指标,建立了以网损最小、静态电压稳定裕度最大为目标的多目标无功优化模型,并将该算法用于IEEE-14系统,结果验证了模型和算法的有效性。
With the development of national economy, the demands of power supply quality from all kinds of industries are increased. Reactive power optimization is one of the most important control methods to ensure power system operation securely and economically, and an effective measure to improve the voltage profile and reduce the transmission loss. Study the problem of reactive power optimization has the great significance in theory and practical application.
Reactive power optimization is a large-scale nonlinear optimization problem with a large number of objects, variables and uncertain parameters, the operating variables include continuous and discrete variables, so the optimization becomes very complex. This paper introduces the process of development and research actuality of reactive power optimization in power system, summarizes the methods of reactive power optimization; The methods mainly include two kinds: sutra and modern. The sutra method mainly refers to definitive searchable method, the modern method involve the manpower intelligence method (especially the genetic algorithm), the tabu searchable method and the simulated anneal etc.
But the methods have some defects and limitations in certain degree, this paper discusses the characteristics of reactive power optimization thorough, establishes the basic mathematical model and the object function of model is based on active power loss, and puts forward a novel algorithm-quantum genetic algorithm (QGA) which is applied to the problem of reactive power optimization of power system. Quantum Genetic Algorithm is a new optimum method that combines quantum computation with Genetic Algorithms. It uses quantum bit as coding which is different from conventional binary coding completely. QGA makes full use of superposition of quantum states to keep diversity of population and avoid prematurity of population. So it has good characteristics of strong search capability, rapid convergence and no premature. It appears strong life-force and valuable for research. It can greatly improve the computation efficiency of GA and remedy shortcoming.
QGA is applied to the problem of reactive power optimization of power system, the computing results against the IEEE-14 and IEEE-30 criterion testing system prove that method of reactive power optimization based on QGA proposed in this paper is effective. Compared with the traditional genetic algorithm, QGA has good characteristics of strong search capability, rapid convergence and no premature, is superior to conventional genetic algorithms in quality and efficiency.
This paper incorporates the voltage stability view to reactive power dispatch and control problem, a model of multi-objective reactive power optimization is established, which takes into account of loss minimization, voltage stability margin maximization and high service quality. The simulations are carried out on IEEE-14 bus system, and the results show the validity of the proposed model and algorithm.
引文
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[33]Lu Wang,X.Rong Li.Robust fast decoupled power flow.IEEE transaction on Dower systems,2002.2.Vol.15(1):208-216
[34]苏琳.基于改进遗传算法的电力系统无功优化.西南交通大学硕士论文,2006,5
[35]万盛斌.基于改进遗传算法的无功优化研究.浙江工业大学硕七论文,2005,5
[36]夏培肃.量子计算.计算机研究与发展,2001,38(10):1153-1171
[37]Hey T.Quantumcomputing:An introduction.Computing&Control Engineering,Journal,1996,10(3):105-112
[38]Narayanan A.An introductory tutorial to quantum computing[A].Proc of IEE Colloquium on Quantum Computing:Theory,Applications and Implications[C]. London:IEEE Press,1997,1/1-1/3
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[43]Tony Hey,Quantum Computing:anintroduction,Computing& Control Engineering Journal,June 1999,105-112
[44]杨俊安,庄镇泉.多宇宙并行量子遗传算法.电子学报,2004,6(6):923-928
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[2]万盛斌,陈明军.基于改进遗传算法的电力系统无功优化.继电器,2005(8):37-40
[3]张伯明.高等电力网络分析.北京,清华大学出版社,1996:241-253
[4]赵晋泉.改进最优潮流牛顿算法有效性的对策研究.中国电机工程学报,1999,Vol.19.No.12:70-75
[5]赵晋泉.牛顿最优潮流算法中离散控制量处理的新方法.电力系统自动化,1999,Vol.23.No.23:37-40
[6]刘明波,陈学军.基于原对偶仿射尺度内点法的电力系统无功优化算法.电网技术,1998,22(3):21-28
[7]沈如刚.电力系统无功功率综合优化—二次规划法.中国电机工程学报,1986,6(5):40-47
[8]赵尤新,徐国禹.灵敏度法分析计算电力系统无功和电压最优控制问题.重庆大学学报,1985(2):1-11
[9]何加坤,张庆安.用全面敏感度分析方法进行电力系统无功综合优化配置.中国电机工程学报,1985,5(3):36-46
[10]张清益.电网分析和发电计划在EMS和DMS中的应用.湖南计算机用户协会,1994(3):156-166
[11]AOKI.K,FAN M,NISHIKORI A.Optimal VAR Planning by Approximation Method for Recursive Mixed-lnteger Linear Programming.IEEE Trans on PAS,1988,3(4):1741-1747
[12]李乃湖.计及整型控制变量的电压一无功功率优化.电力系统自动化,1994,18(12):5-11
[13]张鹏,刘玉田.配电系统电压控制和无功优化的简化动态规划法.电力系统及其自动化学报,1999,11(4):49-54
[14]刘玉田,马莉.基于Tabu搜索方法的电力系统无功优化.电力系统自动化,2000,24(2):61-64
[15]Abdul-Rahman K.H.,Shahidehpour S.M.,Daneshdoost M.An approach to optimal var control with fuzzy reactive loads.IEEE Trans on Power Systems.1995,10(1):256-259
[16]文福栓,韩祯祥.人工神经元网络模型的无功电源最优分布及经济调度.中国电机 工程学报,1992,12(3):20-28
[17]TOMSOVIC K.A fuzzy linear programming approach to the reactive power/voltage control problem.IEEE Trans on Power Systems,1992,7(1):287-293
[18]HUS Y Y,HO K L.Voltage control using a combined integer Linear programming and a rule based approach.IEEE Trans on Power Systems,1992,7(2):744-751
[19]周双喜,杨彬.实现无功优化的新算法—遗传算法.电力系统自动化,1995,19(11):19-23
[20]Kenji Iba.Reactive Power Optimization by Genetic Algorithm.IEEE Trans on Power System,1994,9(2):685-692
[21]张粒子,舒隽,林宪枢.基于遗传算法的无功规划优化.中国电机工程学报,1995,5(5):347-353
[22]方鸽飞,王惠祥,黄晓烁.改进遗传算法在无功优化中的应用.电力系统及其自动化学报,2003,15(4):15-18.
[23]陈皓勇,王锡凡.电力系统无功优化的退火选择遗传算法.中国电力.1998,31(2):3-6
[24]熊信银,吴耀武.遗传算法及其在电力系统中的应用,第一版.武汉:华中科技大学出版社,2002:134-197
[25]苏玲,赵冬梅.电力系统无功优化算法综述.现代电力,2004,12
[26]何仰赞,温增银,汪馥英,周勤慧.电力系统分析[M].武汉:华中理工大学出版社,1985
[27]T.J.E.米勒.电力系统无功功率控制[M].北京:水利电力出版社,1990
[28]何利拴,邱国跃.电力系统无功功率与有功功率控制[M].重庆:重庆大学出版社,1995
[29]程浩忠,吴浩.电力系统无功电压稳定性.中国电力出版社,2004,3
[30]张利国.浅谈电力系统中电压无功功率的控制.广东电力,2005,18(8):16-19
[31]何仰赞,温增银等.电力系统分析(下册).武汉:华中理工大学出版社,1996,7:35-88
[32]诸骏伟.电力系统分析(上册).北京:水利电力出版利,1995,11:2-18
[33]Lu Wang,X.Rong Li.Robust fast decoupled power flow.IEEE transaction on Dower systems,2002.2.Vol.15(1):208-216
[34]苏琳.基于改进遗传算法的电力系统无功优化.西南交通大学硕士论文,2006,5
[35]万盛斌.基于改进遗传算法的无功优化研究.浙江工业大学硕七论文,2005,5
[36]夏培肃.量子计算.计算机研究与发展,2001,38(10):1153-1171
[37]Hey T.Quantumcomputing:An introduction.Computing&Control Engineering,Journal,1996,10(3):105-112
[38]Narayanan A.An introductory tutorial to quantum computing[A].Proc of IEE Colloquium on Quantum Computing:Theory,Applications and Implications[C]. London:IEEE Press,1997,1/1-1/3
[39]Narayanan A & Moore M,Qnantum-inspired genetic algorithm[A].Pros of IEEEE International Conference on Evolutionary Computation[C]Piscataway:IEEE Press,1996,61-66
[40]Han K H,Kim J H.Genetic quantum algorithm and its to application to combinatorial optimization problems[A].Pros of IEEE Conference on Evolutionay Computation[C].Piscataway:IEEE Press,2000,1354-1360
[41]Han K H,Park K H,et al.Parallel quantum-inspired genetic algorithm for combinatorial optimization problems[A].Pros of the IEEE Conference on Evolntionay Computation[C].Piscataway:IEEE Press,2001,1442-1429
[42]李承祖等.量子通信和量子计算.长沙:国防科技大学出版社,2000
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