摘 要
随着电力系统规模的不断扩大和负荷特性的日益复杂，无功补偿设备的优化配置已成为保障电网安全稳定运行的关键问题。本研究针对传统无功补偿配置方法存在的计算效率低、适应性差等问题，提出了一种基于改进粒子群算法的无功补偿设备优化配置方法。通过建立考虑电压稳定性、网络损耗和经济性等多目标优化模型，引入自适应惯性权重和动态学习因子策略，有效提升了算法的全局搜索能力和收敛速度。研究采用IEEE 30节点系统进行仿真验证，结果表明所提方法较传统方法在电压偏差降低15.3%、网损减少8.7%的同时，将计算时间缩短了42.5%。此外，通过灵敏度分析确定了关键节点的最优补偿容量范围，为实际工程应用提供了理论依据。
关键词：无功补偿 粒子群算法 多目标优化
Abstract
With the continuous expansion of the power system scale and the increasing complexity of the load characteristics, the optimal configuration of the reactive power compensation equipment has become a key problem to ensure the safe and stable operation of the power grid. In this paper, the problem is low computing efficiency and poor adaptability. By establishing a multi-ob jective optimization model considering voltage stability, network loss and economy, and introducing adaptive inertial weight and dynamic learning factor strategy, the global search ability and convergence speed of the algorithm are effectively improved. The IEEE 30 node system was used for simulation verification, and the results show that the proposed method reduces the calculation time by 42.5% while the voltage deviation by 15.3% and the network loss by 8.7%. Moreover, the optimal compensation capacity range of critical nodes is determined by sensitivity analysis, which provides a theoretical basis for practical engineering applications.
Keyword: SVC Particle population algorithm multi ob jective optimization
目 录
1绪论 1
1.1研究背景 1
1.2研究意义 1
1.3研究现状 1
2电力系统无功补偿设备特性分析 2
2.1无功补偿设备的分类与工作原理 2
2.2不同补偿设备的性能比较 2
2.3无功补偿对电力系统的影响分析 3
3无功补偿设备优化配置模型构建 3
3.1优化配置的目标函数建立 3
3.2约束条件的确定与分析 4
3.3基于遗传算法的求解方法设计 4
4无功补偿设备优化配置实例分析 5
4.1典型电力系统案例分析 5
4.2优化配置方案的实施效果评估 6
4.3不同场景下的配置策略比较 6
5结论 7
参考文献 8
致谢 9
随着电力系统规模的不断扩大和负荷特性的日益复杂，无功补偿设备的优化配置已成为保障电网安全稳定运行的关键问题。本研究针对传统无功补偿配置方法存在的计算效率低、适应性差等问题，提出了一种基于改进粒子群算法的无功补偿设备优化配置方法。通过建立考虑电压稳定性、网络损耗和经济性等多目标优化模型，引入自适应惯性权重和动态学习因子策略，有效提升了算法的全局搜索能力和收敛速度。研究采用IEEE 30节点系统进行仿真验证，结果表明所提方法较传统方法在电压偏差降低15.3%、网损减少8.7%的同时，将计算时间缩短了42.5%。此外，通过灵敏度分析确定了关键节点的最优补偿容量范围，为实际工程应用提供了理论依据。
关键词：无功补偿 粒子群算法 多目标优化
Abstract
With the continuous expansion of the power system scale and the increasing complexity of the load characteristics, the optimal configuration of the reactive power compensation equipment has become a key problem to ensure the safe and stable operation of the power grid. In this paper, the problem is low computing efficiency and poor adaptability. By establishing a multi-ob jective optimization model considering voltage stability, network loss and economy, and introducing adaptive inertial weight and dynamic learning factor strategy, the global search ability and convergence speed of the algorithm are effectively improved. The IEEE 30 node system was used for simulation verification, and the results show that the proposed method reduces the calculation time by 42.5% while the voltage deviation by 15.3% and the network loss by 8.7%. Moreover, the optimal compensation capacity range of critical nodes is determined by sensitivity analysis, which provides a theoretical basis for practical engineering applications.
Keyword: SVC Particle population algorithm multi ob jective optimization
目 录
1绪论 1
1.1研究背景 1
1.2研究意义 1
1.3研究现状 1
2电力系统无功补偿设备特性分析 2
2.1无功补偿设备的分类与工作原理 2
2.2不同补偿设备的性能比较 2
2.3无功补偿对电力系统的影响分析 3
3无功补偿设备优化配置模型构建 3
3.1优化配置的目标函数建立 3
3.2约束条件的确定与分析 4
3.3基于遗传算法的求解方法设计 4
4无功补偿设备优化配置实例分析 5
4.1典型电力系统案例分析 5
4.2优化配置方案的实施效果评估 6
4.3不同场景下的配置策略比较 6
5结论 7
参考文献 8
致谢 9
