CAJ | 학술논문

对于规模越来越大的复杂电力系统来说,采用基于量测数据的低频振荡研究方法日益受到重视。经验模态分解（EMD）方法的分解过程具有自适应且适于分析非平稳信号,在低频振荡参数提取方面应用较多,但EMD方法存在模态混叠等现象。当信号中2个单频分量的频率在2倍频内时,频移经验模态分解（FS-EMD）可将2个分量分解开。但当信号中有多个单频分量的频率在2倍频内时,FS-EMD就无法分解。为了提高EMD的频率分辨率并使分解方法具有通用性,文中提出了改进的频移经验模态分解（RFS-EMD）算法。此方法增大了信号中组成分量的频率比,且保证频率不翻转,使之可循环使用RFS-EMD算法分解复杂信号。该方法在应用于电力系统低频振荡模态参数的提取时,能较好地提取多个2倍频范围内的低频振荡模态分量的频率、幅值、相位及阻尼比等参数。数值仿真和实例分析均表明了该方法的有效性。
대우규모월래월대적복잡전력계통래설,채용기우량측수거적저빈진탕연구방법일익수도중시。경험모태분해（EMD）방법적분해과정구유자괄응차괄우분석비평은신호,재저빈진탕삼수제취방면응용교다,단EMD방법존재모태혼첩등현상。당신호중2개단빈분량적빈솔재2배빈내시,빈이경험모태분해（FS-EMD）가장2개분량분해개。단당신호중유다개단빈분량적빈솔재2배빈내시,FS-EMD취무법분해。위료제고EMD적빈솔분변솔병사분해방법구유통용성,문중제출료개진적빈이경험모태분해（RFS-EMD）산법。차방법증대료신호중조성분량적빈솔비,차보증빈솔불번전,사지가순배사용RFS-EMD산법분해복잡신호。해방법재응용우전력계통저빈진탕모태삼수적제취시,능교호지제취다개2배빈범위내적저빈진탕모태분량적빈솔、폭치、상위급조니비등삼수。수치방진화실례분석균표명료해방법적유효성。
For large complex power systems, low-frequency oscillation research methods based on measurements have received much attention. Empirical mode decomposition （EMD） is a multi-resolution signal-processing method, and it can be used for non-stationary signals analysis. Although EMD is frequently used in the extraction of low-frequency oscillation parameters, it has disadvantages of model mixing, etc. As .more than two individual components in a signal with frequencies within an octave can be indecomposable by frequency shift EMD （FS-EMD） method, a refined frequency shift EMD （RFS-EMD） is presented to improve the frequency resolution and make FS-EMD more robust. The proposed method is able to enlarge the component frequency ratios, and ensure that the frequency does not turn over, so that the complex signal can be decomposed through repeating this method. In its application for extracting model parameters of low frequency oscillation in a power system, this method proves fairly effective in extracting model parameters in a signal with several frequencies within an octave respectively, such as frequency, amplitude, phase and damping ratio, etc, as shown by numerical simulation and case study results.