| 基于改进的PSO算法的电力系统无功优化研究 |
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| 引用本文： | 徐善伟,侯姗,祁美华.基于改进的PSO算法的电力系统无功优化研究[J].水电能源科学,2012,30(11):188-190,183. |
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| 作者姓名： | 徐善伟 侯姗 祁美华 |
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| 作者单位： | 晋中职业技术学院 机电工程系， 山西 晋中 030600;晋中职业技术学院 机电工程系， 山西 晋中 30600；太原科技大学 研究生院， 山西 太原 030024;晋中职业技术学院 机电工程系， 山西 晋中 030600;太原理工大学 电气与动力工程学院， 山西 太原 030024 |
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| 摘 要： | 电力系统无功优化是保证电力系统安全、经济运行的重要措施，粒子群优化算法（PSO）具有模型简单、收敛速度快、参数简洁等优点，但用于求解高维复杂优化问题时易陷入局部最优，针对此缺陷，在PSO算法的基础上提出了自适应随机变异粒子群优化算法（AMPSO），将该算法用于求解电力系统无功优化问题，并以IEEE30标准节点系统为算例进行验证。结果表明，与PSO算法相比，AMPSO算法有效降低了系统网损，显现出良好的全局收敛特性。
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| 关 键 词： | 粒子群优化算法 电力系统 无功优化 自适应随机变异粒子群优化算法 |
Research on Improved PSO Algorithm for Power System Reactive Power Optimization |
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| XU Shanwei,HOU Shan and QI Meihua.Research on Improved PSO Algorithm for Power System Reactive Power Optimization[J].International Journal Hydroelectric Energy,2012,30(11):188-190,183. |
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| Authors: | XU Shanwei HOU Shan and QI Meihua |
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| Affiliation: | Department Mechanical and Electrical Engineering, Jinzhong ocational and Technical College, Jinzhong 030601, China;Department Mechanical and Electrical Engineering, Jinzhong ocational and Technical College, Jinzhong 030601, China; School of Graduate, Taiyuan University of Science and Technology, Taiyuan 030024, China;Department Mechanical and Electrical Engineering, Jinzhong ocational and Technical College, Jinzhong 030601, China;College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China |
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| Abstract: | Reactive power optimization is of great importance to guarantee security and economic operation of power system. Particle swarm optimization (PSO) has some advantages of simple model, fast convergence and simple parameter. When PSO is used to solve high-dimensional complex optimization problem, it is easy to fall into local optimum. So, an adaptive mutation particle swarm optimization algorithm (AMPSO) is proposed to solve reactive power optimization in power system. An IEEE30 node system is used to verify the AMPSO algorithm. The results show that AMPSO reduce system losses effective and it appears good global convergence. |
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| Keywords: | particle swarm optimization algorithm power system reactive power optimization AMPSO algorithm |
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