论文总字数:33492字
摘 要
现如今的电网复杂,这就让我们越来越注重配电网供电的可靠性还有电压的质量问题了。虽然当前大家都认为无功优化可以有效提高电能质量、降低网损、充分保证电力系统运行的稳定性。然而,随着研究人员们的研究,传统的无功优化算法已经不能满足如今复杂的电网无功的优化要求。因此由于人工智能算法在当今的不断发展,所以智能算法对无功优化也顺势变成了当今研究人员的研究发展方位。在这当中,粒子群优化算法由于其算法的结构较为简单、收敛速度也快等各个优势,也因此在电力系统无功优化问题当中获得了越来越多人们的关注,可是这个算法到了运行计算的后期,却非常容易陷入局部最优值,会导致崩溃,收敛速度变慢,故需要进一步改善。另外一方面,由于近年来非线性设备在电网中的应用越来越广泛,产生的谐波污染对电气设备的正常运行会产生很大的影响。因此,考虑谐波潮流影响下的电网无功优化研究,合理配置电网无功优化,有利于提高电能质量、降低网损,同时抑制谐波对电网安全经济运行的影响。综上所述,本文针对考虑谐波潮流影响的电网无功优化研究,主要进行了电网无功优化模型的建立、掌握一定的电网谐波潮流的计算、求解电网无功优化的AI智能优化算法的研究、建立考虑谐波潮流影响的电网无功优化模型并进行仿真计算。同时,本文最后采用了IEEE11节点进行改进算法的仿真分析。仿真结果表现出来的是本文所建立的模型和改进的算法对无功优化具有较好的效果。
关键词:无功优化;配电网;谐波;潮流算法;粒子群优化算法
Research on Reactive Power Optimization of Distribution Network Considering the Influence of Harmonic Power Flow
Abstract
Nowadays, the power grid is complex, which makes us pay more and more attention to the reliability of power distribution network and the quality of voltage. Although everyone currently believes that reactive power optimization can effectively improve power quality, reduce network losses, and fully ensure the stability of power system operation. However, with the research of researchers, traditional reactive power optimization algorithms can no longer meet the requirements of today"s complex reactive power optimization. Therefore, due to the continuous development of artificial intelligence algorithms today, the optimization of reactive power by intelligent algorithms has also become the research and development direction of today"s researchers. Among them, the particle swarm optimization algorithm has attracted more and more attention in the reactive power optimization problem of power system due to its simple structure and fast convergence speed. However, this algorithm has reached the operating calculation. In the later stage, it is very easy to fall into the local optimal value, which will lead to collapse and slower convergence speed, so further improvement is needed. On the other hand, due to the increasing application of nonlinear equipment in the power grid in recent years, the generated harmonic pollution will have a great impact on the normal operation of electrical equipment. Therefore, considering the research of grid reactive power optimization under the influence of harmonic power flow, reasonable configuration of grid reactive power optimization is conducive to improving power quality, reducing power loss, and restraining the impact of harmonics on the safe and economic operation of the power grid. In summary, for the research on grid reactive power optimization considering the influence of harmonic power flow, this article mainly conducts the establishment of grid reactive power optimization model, masters certain grid harmonic power flow calculations, and solves the AI intelligent optimization algorithm for grid reactive power optimization. The research, establishment of grid reactive power optimization model considering the influence of harmonic power flow and simulation calculation. At the same time, at the end of this paper, IEEE11 nodes are used for simulation analysis of the improved algorithm. The simulation results show that the model established in this paper and the improved algorithm have a good effect on reactive power optimization.
Keywords: Reactive Power Optimization, Distribution Network, Harmonic, Power Flow Algorithm, Particle Swarm Optimization Algorithm
目 录
摘 要 I
Abstract II
第一章 绪 论 1
1.1 选题的背景和意义 1
1.2 谐波对电网的影响 1
1.3 配电网无功补偿配置优化问题的研究现状 2
1.3.1 国内外无功优化算法发展及现状 2
1.3.2 配电网无功补偿配置优化待研究问题 3
1.4 本文的主要工作 4
第二章 电网谐波的潮流计算 5
2.1 引言 5
2.2 辐射状配网的前推回代潮流计算算法 5
2.3 谐波潮流计算模型及算法 7
2.3.1 谐波潮流计算模型 7
2.3.2 谐波网络方程 9
2.3.3 常见谐波潮流计算方法 10
2.3.4 前推回代谐波潮流计算 12
2.4 本章小结 13
第三章 求解配电网无功优化问题的智能算法的研究 14
3.1 粒子群优化算法的研究 14
3.1.1 算法的起源 14
3.1.2 算法的介绍 14
3.2 粒子群优化算法的改进 17
3.2.1 粒子群优化算法的改进方法 17
3.2.2 粒子群优化算法改进后的运算步骤 19
3.3 本章小结 20
第四章 建立考虑谐波潮流影响的电网无功优化模型和算法 21
4.1 引言 21
4.2 考虑谐波潮流影响的电网无功优化模型 21
4.2.1 目标函数 21
4.2.2 系统潮流约束 23
4.2.3 控制变量约束 23
4.2.4 状态变量约束 23
4.3 基于改进粒子群优化算法的求解方法 24
4.3.1 控制变量的处理 24
4.3.2 解空间以及初始解群体的改进 24
4.3.3 改进粒子群优化算法的无功优化模型的步骤 25
4.4 本章小结 26
第五章 算例分析 27
5.1 IEEE11节点优化分析 27
5.2本章小结 34
第六章 本文总结 35
致 谢 36
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