| 基于量子粒子群算法的电力系统无功优化 |
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| 引用本文： | 宋云峰,李扬,刘新伟. 基于量子粒子群算法的电力系统无功优化[J]. 东北电力技术, 2011, 32(5): 1-4 |
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| 作者姓名： | 宋云峰 李扬 刘新伟 |
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| 作者单位： | 宋云峰,SONG Yun-feng(大连供电公司,辽宁,大连,116001);李扬,刘新伟,LI Yang,LIU Xin-wei(东北电力大学,吉林,吉林,132012) |
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| 摘 要： | 量子粒子群优化算法(QPSO)避免了粒子群算法(PSO)不能保证收敛到全局最优解的缺点,认为粒子具有量子的行为,并且可以在整个可行解空间进行搜索.无功优化问题是带有离散变量的非线性、不连续、多约束、多变量的复杂优化问题,应用QPSO算法并结合动态调整罚函数的方法来解决无功优化问题.通过对IEEE-30节点和IEEE-1...
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| 关 键 词： | 量子粒子群算法 全局最优 无功优化 动态罚函数 |
Reactive Power Optimization of the Power System Based on Quantum-behaved Particle Swarm Optimization |
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| SONG Yun-feng,LI Yang,LIU Xin-wei. Reactive Power Optimization of the Power System Based on Quantum-behaved Particle Swarm Optimization[J]. Northeast Electric Power Technology, 2011, 32(5): 1-4 |
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| Authors: | SONG Yun-feng LI Yang LIU Xin-wei |
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| Affiliation: | SONG Yun-feng LI Yang LIU Xin-wei |
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| Abstract: | Unlike Particle swarm optimization,quantum-behaved particle swarm optimization(QPSO)can assure the convergence of global optimality,because particles have quantum-behave,and is able to carry out feasible solution search.Reactive power optimal has a series complicated problems such as discrete variables of linear,discontinuous,multiple constraints,multiple variables,QPSO combined with dynamic adjusting of penalty function is used to optimize reactive power.By simulation of IEEE-30 node and IEEE-14 node,comparing them with PSO and GA algorithm,the result shows QPSO can do better in global optimal solution. |
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| Keywords: | Quantum-behaved particle swarm optimization Global optimal Reactive power Dynamic adjusting of penalty function |
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