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文章摘要
基于量子行为粒子群优化算法的电压暂降状态估计
Voltage Sag State Estimation in Power System Based on Quantum-Behaved Particle Swarm Optimization
Received:July 25, 2014  Revised:July 25, 2014
DOI:
中文关键词: 电能质量  电压暂降状态估计  故障位置法  量子行为粒子群优化  遗传算法
英文关键词: power  quality, voltage  sag state  estimation, fault  position method, QPSO, genetic  algorithm
基金项目:
Author NameAffiliationE-mail
LIU Yi-jun* School of Electrical and Information,Sichuan University 390892761@qq.com 
Yang Hong-geng School of Electrical and Information,Sichuan University  
Wang Jia-xing School of Electrical and Information,Sichuan University  
Wang Ze School of Electrical and Information,Sichuan University  
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中文摘要:
      提出了一种将传统的故障位置法和状态估计法相结合的电压暂降状态估计新方法。将仅利用历史故障数据的故障位置法和仅利用现有有限监控数据的状态估计法相结合,得到电压暂降状态方程,并利用量子行为粒子群算法(QPSO)得到优化问题的最优解。对比遗传算法等传统优化算法,QPSO能保证全局收敛,且控制参数更少,随机性更强,则找寻最优解效率更高。该方法已在IEEE24标准节点系统上进行仿真计算,并与遗传算法对比,结果验证了本文方法的准确性和可靠性。此方法适用于任何规模电网发生对称故障和不对称故障时的电压暂降状态估计。
英文摘要:
      a new method of voltage sag state estimation based on the combination of the traditional fault position method and state estimation method in this paper. To combine the fault position method which only use the historical data and state estimation method which only use the limited monitoring data, voltage state estimation equation is obtained.And use quantum-behaved particle swarm optimization to solve the optimization problem in this paper. Compared with traditional optimization algorithm such as GA, the proposed algorithm can guarantee the global convergence, and has less control parameters and stronger randomness , so it’s more efficient to find the optimal solution. The proposed estimation method has been validated by using the IEEE 24 test system and the results obtained have been very satisfactory. The proposed method is applicable to voltage sag state estimation in any size grids which occurs symmetric and asymmetric faults.
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