基于混沌粒子群算法的同步发电机最优调速控制系统
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摘要
随着电力系统规模的日益扩大,保证系统运行的可靠性和稳定性,提供合格的电能质量和良好的动态品质具有极其重要的意义。同步发电机调速控制系统是电力系统控制的重要组成部分,它能够起到减小系统频率波动、平衡有功功率、维护系统有功功率稳定等重要作用。同步发电机的调速系统通过调节原动机向发电机输出的机械功率,从而实现功率和频率的调节,保持电网的正常运行频率。运用自动控制理论和优化理论对同步发电机的调速系统进行适当的控制,对于建立动态品质良好、稳定性高的电力系统的有功功率稳定性有重要作用。
本文对同步发电机调速控制系统已有研究成果的综合分析的基础上,建立了有调速系统的中间再热式汽轮机数学模型和整个调速控制系统得状态方程。该状态方程的建立,为基于混沌粒子群算法的同步发电机最优调速控制系统的仿真打下了基础。
本文在了解和研究传统粒子群算法的优化原理的基础上,针对电力系统的特点,对传统的调速控制系统进行了改进。本文采用了基于混沌搜索的粒子群算法,该算法以粒子群算法作为主体,对种群中的最好粒子进行给定步数的混沌优化搜索,指导粒子群向最优解方向搜索。该算法改善了传统的粒子群算法容易局部收敛,得不到全局最优解和精确度不高的缺点。并在此基础上,本文将混沌粒子群算法用于同步发电机最优调速控制器设计,采用混沌粒子群算法对调速控制器的参数进行优化,设计出基于混沌粒子群算法的同步发电机最优调速器。
本文对单机——无穷大系统在机械扰动和三相短路故障情况下进行仿真试验。通过比较传统的、基于粒子群算法的和基于混沌粒子群算法的调速控制系统频率随时间的变化曲线,可以看出基于改进粒子群算法的调速控制系统具有对工程经验依赖小,简单实用,对于偏离设计运行点的工况,仍能表现出良好的动态调节性能,具有很强的鲁棒性的优点。
本文在对单机系统研究的基础上,建立了简单的互联电力系统的模型,并进行仿真分析。通过对互联系统单区域扰动的仿真,进一步证明了基于改进粒子群算法的调速控制系统的优越性。同时,通过比较互联前后故障区域的频率变化情况,验证了网络互联对于提高电力系统频率稳定的积极意义。
With the expansion of power system scale, it becomes more and more important to keep power system’s reliability and stability, as well as super performance and dynamic quality. Synchronous generator governor is an important part of power system operation. It is designed to reduce frequency pulsation, balance the distribution of active power and steady operation of the system. Synchronous generator governor regulates the mechanical power from prime motor to generator in order to realize the power and frequency adjustment and keep the normal operating frequency. It is important to build a power system with good dynamic quality and stability by using the controlling the governor bell crank properly by the principles of automatic control and operation theory.
On the base of the research of synchronous generator governor, this paper firstly established the mathematic model of a double-reheat steam turbine including governor. Then the corresponding synchronous generator state equations including the governor and quick-response excitation can be got. The establishment of the system state equations is the basis of the dynamic emulation experiments of speed control system.
Based on the particle swarm theory and characters of the power system, this paper improves the speed control system with chaos particle swarm algorithm. This modified optimization algorithm. The particle swarm algorithm was choose as the main part, and prescribed number steps chaos search was taken on the best particle. The global optimum solution can be got and precision can be improved by using this algorithm. In this paper a new design of speed control system for synchronous generator based on chaos particle swarm algorithm was proposed in this paper and a new speed control system was designed on the basic of chaos particle swarm algorithm,which use chaos particle swarm algorithm for the parameters optimization of it.
Simulation of one machine-infinite system is programmed in two different conditions, they are mechanical disturbance and short-circuit fault. Through comparing the frequency of generator which is controlled separately by traditional PID controller, controller based on particle swarm algorithm and controller based on the chaos particle swarm algorithm, the conclusion could be got that the last controller has the advantage as follows: it is independent to the project experience, simple and practical, additional, it can still get a very good output even if the generator operates in the deviating point, that means it has a good robustness.
A simple inter-connected system model is established in this paper on the basic of the one machine-infinite system. Through the simulation which inference happens in one scale, the superiority of the controller based on modified particle swarm algorithm is certificated again. Also through the compare of the frequency curve between pre-fault and post-fault, the advantage of the inter-connected system is shown.
本文对同步发电机调速控制系统已有研究成果的综合分析的基础上,建立了有调速系统的中间再热式汽轮机数学模型和整个调速控制系统得状态方程。该状态方程的建立,为基于混沌粒子群算法的同步发电机最优调速控制系统的仿真打下了基础。
本文在了解和研究传统粒子群算法的优化原理的基础上,针对电力系统的特点,对传统的调速控制系统进行了改进。本文采用了基于混沌搜索的粒子群算法,该算法以粒子群算法作为主体,对种群中的最好粒子进行给定步数的混沌优化搜索,指导粒子群向最优解方向搜索。该算法改善了传统的粒子群算法容易局部收敛,得不到全局最优解和精确度不高的缺点。并在此基础上,本文将混沌粒子群算法用于同步发电机最优调速控制器设计,采用混沌粒子群算法对调速控制器的参数进行优化,设计出基于混沌粒子群算法的同步发电机最优调速器。
本文对单机——无穷大系统在机械扰动和三相短路故障情况下进行仿真试验。通过比较传统的、基于粒子群算法的和基于混沌粒子群算法的调速控制系统频率随时间的变化曲线,可以看出基于改进粒子群算法的调速控制系统具有对工程经验依赖小,简单实用,对于偏离设计运行点的工况,仍能表现出良好的动态调节性能,具有很强的鲁棒性的优点。
本文在对单机系统研究的基础上,建立了简单的互联电力系统的模型,并进行仿真分析。通过对互联系统单区域扰动的仿真,进一步证明了基于改进粒子群算法的调速控制系统的优越性。同时,通过比较互联前后故障区域的频率变化情况,验证了网络互联对于提高电力系统频率稳定的积极意义。
With the expansion of power system scale, it becomes more and more important to keep power system’s reliability and stability, as well as super performance and dynamic quality. Synchronous generator governor is an important part of power system operation. It is designed to reduce frequency pulsation, balance the distribution of active power and steady operation of the system. Synchronous generator governor regulates the mechanical power from prime motor to generator in order to realize the power and frequency adjustment and keep the normal operating frequency. It is important to build a power system with good dynamic quality and stability by using the controlling the governor bell crank properly by the principles of automatic control and operation theory.
On the base of the research of synchronous generator governor, this paper firstly established the mathematic model of a double-reheat steam turbine including governor. Then the corresponding synchronous generator state equations including the governor and quick-response excitation can be got. The establishment of the system state equations is the basis of the dynamic emulation experiments of speed control system.
Based on the particle swarm theory and characters of the power system, this paper improves the speed control system with chaos particle swarm algorithm. This modified optimization algorithm. The particle swarm algorithm was choose as the main part, and prescribed number steps chaos search was taken on the best particle. The global optimum solution can be got and precision can be improved by using this algorithm. In this paper a new design of speed control system for synchronous generator based on chaos particle swarm algorithm was proposed in this paper and a new speed control system was designed on the basic of chaos particle swarm algorithm,which use chaos particle swarm algorithm for the parameters optimization of it.
Simulation of one machine-infinite system is programmed in two different conditions, they are mechanical disturbance and short-circuit fault. Through comparing the frequency of generator which is controlled separately by traditional PID controller, controller based on particle swarm algorithm and controller based on the chaos particle swarm algorithm, the conclusion could be got that the last controller has the advantage as follows: it is independent to the project experience, simple and practical, additional, it can still get a very good output even if the generator operates in the deviating point, that means it has a good robustness.
A simple inter-connected system model is established in this paper on the basic of the one machine-infinite system. Through the simulation which inference happens in one scale, the superiority of the controller based on modified particle swarm algorithm is certificated again. Also through the compare of the frequency curve between pre-fault and post-fault, the advantage of the inter-connected system is shown.
引文
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[28]李建勇,粒子群优化算法研究:[硕士学位论文],浙江;浙江大学,2004
[29]郑泳凌,计算智能在控制、优化和决策中的应用研究:[硕士学位论文],浙江;浙江大学,2004
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[4] Eberhart, R. and Kennedy, J. A new optimizer using particle swarm theory[C], Proc 6 International Symposium on Machine and Human Science, 1995, pp. 39-43. Ourari, M.L, Dessaint, L.A, Do, V.Q. Generating units aggregation for dynamic equivalent of large power systems. Power Engineering Society General Meeting, 2004, 2(1): 1535~1541
[5] Kennedy, J. and Eberhart, R. Particle Swar, Optimization[C], IEEE International Conference on Networks (Perth , Australia), IEEE Service Center, Piscataway, NJ, 1995, IV: 1942~1948
[6]樊玮,粒子群优化方法及其实现,航空计算技术,2004:34(3)39~42
[7]王冬梅,群体智能优化算法的研究:[硕士学位论文],武汉;武汉科技大学,2004
[8]杨俊杰,周建中,吴玮,刘芳,改进粒子群优化算法在负荷经济分配中的应用,2005,29(2):1~4
[9] Jong-Bae Park, Ki-Song Lee.Econimic load dispatch for non-smooth cost functions using particle swarm optimization[C]. Ontario, Canada: IEEE Power Engineering Society General Meeting, 2003: 938~943.
[10]赵波,曹一家,电力系统无功优化的多智能体粒子群优化算法,中国电机工程学报.2005,25(5):1~7
[11]周晖,周任军,谈顺涛,周皓,用于无功综合控制的改进粒子群优化算法,电网技术,2004,28(13):45~49
[12] Sensarma P S, Rahmani M.A comprehensive method for optimal expansion planning using particle swarm optimization[C]. New York, USA: Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, 2002:1317~1322
[13]金义雄,程浩忠,严建勇等,改进粒子群算法及其在输电网规划中的应用[J],中国电机工程学报,2005,25(4):46~50
[14] Zwe-Lee Gaing. Discrete particle swarm optimization algorithm for unit commitment[C]. Ontario, Canada: IEEE Power Engineering Society GeneralMeeting, 2003: 418~424
[15] Chin Aik Koay, Dipti Srinivasan. Particle swarm optimization-based approach for generator maintenance scheduling[A]. Proceedings of the IEEE Swarm Intelligence Symposium 2003[C]. Indiana, USA, 2003:167~173
[16]薛定宇,控制系统计算机辅助设计,北京:清华大学出版社,1996.253~264
[17]韦刚,电力系统分析基础,北京:中国电力出版社,2006.116~133
[18]何仰赞,温增银,电力系统分析,武汉:华中科技大学出版社,2003.110~127
[19]降爱琴,郝秀芳,数字电液调节与旁路控制系统,北京:中国电力出版社,2006.1~18
[20]方红庆,郝培宏,吴恺,水轮机调速器控制技术研究,山西水利科技,2004,15(2):53~55
[21]方红庆,沈祖诒,基于改进粒子群算法的水轮发电机组PID调速器参数优化,中国电机工程学报,2005,25(22):120~124
[22]张艳霞,姜惠兰,电力系统保护与控制,北京:清华大学出版社,2005.199~225
[23]于达仁,毛志伟,徐基豫,汽轮发电机组的一次调频动态特性,中国电机工程学报,1996,16(4):221~225
[24]于达仁,郭钰锋,一次调频的随机过程分析,中国电机工程学报,2002,22(6):38~41
[25]林文孚,胡燕,单元机组自动控制技术,北京:中国电力出版社,2004.125~157
[26]代云修,张灿勇,汽轮机设备及系统,北京:中国电力出版社,2006.96~117
[27]张奎明,时海刚,热工自动化,北京:中国电力出版社,2006.345~381
[28]李建勇,粒子群优化算法研究:[硕士学位论文],浙江;浙江大学,2004
[29]郑泳凌,计算智能在控制、优化和决策中的应用研究:[硕士学位论文],浙江;浙江大学,2004
[30]商国才,电力系统自动化,天津大学出版社,1999.67~123
[31]何利铨,邱国跃,电力系统无功功率与有功功率控制,重庆:重庆大学出版社,1995.125~132
[32]倪以信,陈寿孙,张宝霖,动态电力系统的理论和分析,北京:清华大学出版社,2002.186~191
[33]邹恩,李祥飞,张泰山,混沌控制综述,株洲工学院学报,2002,16(4):16~18
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