| 采用优化经验模态分解的电力谐波辨识方法 |
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| 引用本文： | 吴江伟,王雪,孙欣尧,刘佑达.采用优化经验模态分解的电力谐波辨识方法[J].电子测量与仪器学报,2012,26(10):858-863. |
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| 作者姓名： | 吴江伟 王雪 孙欣尧 刘佑达 |
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| 作者单位： | 清华大学精密仪器与机械学系精密测试技术及仪器国家重点实验室,北京,100084 |
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| 摘 要： | 电力谐波的准确辨识对智能用电具有重要的研究价值和意义。针对基于屏蔽信号的经验模态分解(M-EMD)在谐波辨识中幅值误差较大、模态分解不完整以及屏蔽信号构建参数依赖经验值等问题,提出对待分析信号进行滤波和模态预提取,并采用协同混沌粒子群优化算法(CCPSO)对屏蔽信号的构建参数进行寻优。电力谐波仿真辨识实验证明,与M-EMD算法相比,文中所述的IM-EMD算法在谐波辨识的准确度和可靠性上有了明显提高。
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| 关 键 词： | 谐波辨识 经验模态分解 屏蔽信号 协同混沌粒子群优化 |
The method for harmonic identification based on optimal empirical mode decomposition |
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| Wu Jiangwei
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Wang Xue
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Sun Xinyao
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Liu Youda.The method for harmonic identification based on optimal empirical mode decomposition[J].Journal of Electronic Measurement and Instrument,2012,26(10):858-863. |
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| Authors: | Wu Jiangwei Wang Xue Sun Xinyao Liu Youda |
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| Affiliation: | Wu Jiangwei Wang Xue Sun Xinyao Liu Youda(State Key Laboratory of Precision Measurement Technology and Instruments,Department of Precision Instruments,Tsinghua University,Beijing 100084,China) |
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| Abstract: | Harmonic analysis is very important to smart electricity.This paper proposes a hybrid algorithm to deal with the problems of empirical mode decomposition based masking signal（M-EMD） used in harmonic analysis,such as huge amplitude error,imperfect decomposition and the dependence on empirical values in building masking signal.The proposed method applies the method of chaos cooperation particle swarm optimization（CCPSO） to search proper parameters for masking signals,and employs filter and frequency pre-extract to the original signal.The synthetic experiments demonstrate that the method proposed in this paper gets better accuracy and reliability. |
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| Keywords: | harmonic analysis empirical mode decomposition masking signals chaos cooperation particle swarm optimization |
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