遗传算法在电力系统无功优化中的应用研究.doc
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遗传算法在电力系统无功优化中的应用研究,1.67万字我自己原创的毕业论文，仅在本站独家提交，大家放心使用目录摘要iiabstractiii第一章绪论11.1遗传算法的背景和意义11.1.1遗传算法的发展历史21.1.2遗传算法的研究现状以及特点31.2电力系统无功优化的背景和意义41.2.1电力系统无功优化的研究现状5第...
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遗传算法在电力系统无功优化中的应用研究
1.67万字
我自己原创的毕业论文，仅在本站独家提交，大家放心使用
目 录
摘要 II
Abstract III
第一章 绪论 1
1.1遗传算法的背景和意义 1
1.1.1遗传算法的发展历史 2
1.1.2遗传算法的研究现状以及特点 3
1.2电力系统无功优化的背景和意义 4
1.2.1电力系统无功优化的研究现状 5
第二章 遗传算法 8
2.1遗传算法基本操作 8
2.2 遗传算法的基本定理 9
2.3 遗传算法的解题步骤 12
第三章 遗传算法在无功优化中的应用 15
3.1 无功优化规划模型描述 15
3.2 无功优化的模型求解过程 18
3.3计算实例 20
3.4模型求解——遗传算法及改进 22
第四章 结论与展望 24
4.1结论 24
4.2展望 25
4.2.1 遗传算法的发展趋势 25
4.2.2电力系统无功优化的发展趋势 26
致谢 28
参考文献 29
摘要
进入二十一世纪以来,我国的电力工业迅速发展,电力用户对电能质量的要求越来越高,如何保证现代电力系统的安全、稳定、经济运行成为当代电力工作者面临的一个重要问题。电力系统无功优化能有效地降低电力系统的有功功率损耗、改善电网的电压质量,是保证电力系统安全、稳定、经济运行的重要手段。因此,对电力系统无功优化问题的研究,具有重要的理论指导意义和较高的实际应用价值。电力系统无功优化是一个既含有连续变量又含有离散变量的复杂的非线性规划问题,其求解过程异常繁琐。传统的无功优化算法依赖于精确的数学模型,一般要求目标函数连续、可导,且不能精确的处理离散变量,致使在求解含有大量离散变量的电力系统无功优化问题时产生较大误差,影响了计算结果的准确性。而人工智能优化算法不需要精确的数学模型,就能够很好地处理非线性及离散性问题,因此在优化运算中得到了广泛的应用。
本文在综合分析当前各种传统优化算法和人工智能算法优缺点的基础上,结合电力系统的实际,选取遗传算法作为求解电力系统无功优化问题的方法。针对电力系统无功优化的特点,选取电力系统有功网损最小为目标函数,运用计算速度和精度较高的快速解耦法进行潮流计算。此外,文中还详细介绍了遗传算法的基本原理及电力系统无功优化的目的和意义。
关键词：遗传算法 电力系统 无功优化 潮流计算
Abstract
The twenty-first century, the rapid development of China's power industry , power quality power users have become increasingly demanding , how to ensure the security of modern power systems , stability and economic operation of power has become an important contemporary issues facing workers . Reactive power optimization of power system can effectively reduce the active power loss of the power system , to improve the quality of the grid voltage , the power system is an important means to ensure security, stability and economic operation. Therefore, reactive power optimization problem , has important theoretical significance and high practical value. Reactive power optimization is a complex nonlinear programming problem containing both a continuous variable and discrete variables , the solution process anomalies cumbersome. Traditional reactive power optimization algorithm relies on accurate mathematical model , generally require continuous objective function can lead , and can not accurately handling discrete variables , resulting in solving power system contains a large number of discrete variables produce large errors when the reactive power optimization problem, affect the accuracy of the calculated results . The artificial intelligence optimization algorithm does not require a precise mathematical model , we can deal with nonlinear and discrete problems well , so it has been widely used in the optimization calculation . In this paper, a comprehensive analysis of the current basis of a variety of traditional optimization algorithms and artificial intelligence algorithms advantages and disadvantages, with the actual power system , select the genetic algorithm as a method for solving reactive power optimization problem . For reactive power optimization features , the paper selected power system active power loss minimization objective function , using a penalty function to deal with the constraints of power system state variables , flow calculation using the computing speed and high precision fast decoupled method. In addition, the paper also introduces the basic principles of genetic algorithms and various improvements as well as the purpose and significance of reactive power optimization .
Key words： Genetic Algorithms; Reactive Power Optimization; Flow Calculation
1.67万字
我自己原创的毕业论文，仅在本站独家提交，大家放心使用
目 录
摘要 II
Abstract III
第一章 绪论 1
1.1遗传算法的背景和意义 1
1.1.1遗传算法的发展历史 2
1.1.2遗传算法的研究现状以及特点 3
1.2电力系统无功优化的背景和意义 4
1.2.1电力系统无功优化的研究现状 5
第二章 遗传算法 8
2.1遗传算法基本操作 8
2.2 遗传算法的基本定理 9
2.3 遗传算法的解题步骤 12
第三章 遗传算法在无功优化中的应用 15
3.1 无功优化规划模型描述 15
3.2 无功优化的模型求解过程 18
3.3计算实例 20
3.4模型求解——遗传算法及改进 22
第四章 结论与展望 24
4.1结论 24
4.2展望 25
4.2.1 遗传算法的发展趋势 25
4.2.2电力系统无功优化的发展趋势 26
致谢 28
参考文献 29
摘要
进入二十一世纪以来,我国的电力工业迅速发展,电力用户对电能质量的要求越来越高,如何保证现代电力系统的安全、稳定、经济运行成为当代电力工作者面临的一个重要问题。电力系统无功优化能有效地降低电力系统的有功功率损耗、改善电网的电压质量,是保证电力系统安全、稳定、经济运行的重要手段。因此,对电力系统无功优化问题的研究,具有重要的理论指导意义和较高的实际应用价值。电力系统无功优化是一个既含有连续变量又含有离散变量的复杂的非线性规划问题,其求解过程异常繁琐。传统的无功优化算法依赖于精确的数学模型,一般要求目标函数连续、可导,且不能精确的处理离散变量,致使在求解含有大量离散变量的电力系统无功优化问题时产生较大误差,影响了计算结果的准确性。而人工智能优化算法不需要精确的数学模型,就能够很好地处理非线性及离散性问题,因此在优化运算中得到了广泛的应用。
本文在综合分析当前各种传统优化算法和人工智能算法优缺点的基础上,结合电力系统的实际,选取遗传算法作为求解电力系统无功优化问题的方法。针对电力系统无功优化的特点,选取电力系统有功网损最小为目标函数,运用计算速度和精度较高的快速解耦法进行潮流计算。此外,文中还详细介绍了遗传算法的基本原理及电力系统无功优化的目的和意义。
关键词：遗传算法 电力系统 无功优化 潮流计算
Abstract
The twenty-first century, the rapid development of China's power industry , power quality power users have become increasingly demanding , how to ensure the security of modern power systems , stability and economic operation of power has become an important contemporary issues facing workers . Reactive power optimization of power system can effectively reduce the active power loss of the power system , to improve the quality of the grid voltage , the power system is an important means to ensure security, stability and economic operation. Therefore, reactive power optimization problem , has important theoretical significance and high practical value. Reactive power optimization is a complex nonlinear programming problem containing both a continuous variable and discrete variables , the solution process anomalies cumbersome. Traditional reactive power optimization algorithm relies on accurate mathematical model , generally require continuous objective function can lead , and can not accurately handling discrete variables , resulting in solving power system contains a large number of discrete variables produce large errors when the reactive power optimization problem, affect the accuracy of the calculated results . The artificial intelligence optimization algorithm does not require a precise mathematical model , we can deal with nonlinear and discrete problems well , so it has been widely used in the optimization calculation . In this paper, a comprehensive analysis of the current basis of a variety of traditional optimization algorithms and artificial intelligence algorithms advantages and disadvantages, with the actual power system , select the genetic algorithm as a method for solving reactive power optimization problem . For reactive power optimization features , the paper selected power system active power loss minimization objective function , using a penalty function to deal with the constraints of power system state variables , flow calculation using the computing speed and high precision fast decoupled method. In addition, the paper also introduces the basic principles of genetic algorithms and various improvements as well as the purpose and significance of reactive power optimization .
Key words： Genetic Algorithms; Reactive Power Optimization; Flow Calculation
