摘要
为克服传统方法不能自适应反映振荡模式时变特性的缺点,提出一种分析低频振荡模式的新方法。该方法将粒子群优化算法应用于原子分解的迭代优化过程中,通过比较粒子的最佳适应值,选取最能反映振荡模式特性的模态原子,从而实现将模态原子的选取过程转化为利用粒子群优化算法求解函数最优化问题。每1个模态原子对应于1个振荡模式,最终完成低频振荡模态参数辨识过程。该方法具有较强的抗噪性能,鲁棒性强,并能有效追踪振荡模式的时变特性,时频分辨率强。算例分析表明了该方法的有效性与实用性,优于希尔伯特–黄变换方法。
A novel method for analyzing low frequency oscillation modes was proposed to avoid the disadvantages of traditional methods that could not adaptively reveal the oscillation modes＇ time-varying performance. This method applied particle swarm optimization （PSO） to the atom decomposition process and the best modal atoms were Selected by comparing the particles＇ best values. Thus, the process for selecting modal atoms was turned into solving the function optimization problems by PSO. Each modal atom was corresponding to an oscillation mode and the modal parameters were identified completely. This method has the abilities of anti-noise and robustness. Furthermore, the method can effectively track the oscillation modes＇ time-varying process and the time-frequency resolution is higher. The results demonstrate that the proposed method superior to HHT is effective and applicable.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2013年第10期79-89,15,共11页
Proceedings of the CSEE
基金
中央高校基本科研业务费专项资金资助(201120702020012)~~
关键词
振荡模式
广域测量系统
粒子群优化
原子分解
模态原子
oscillation system
particle swarm decomposition
modal atom mode
wide area measurement optimization （PSO）
atom
