| 引用本文: | 张艳军,殷祥翔,葛延峰,魏俊红,王长江.基于APIT-MEMD的电力系统低频振荡模式辨识新方法[J].电力系统保护与控制,2020,48(14):165-174.[点击复制] |
| ZHANG Yanjun,YIN Xiangxiang,GE Yanfeng,WEI Junhong,WANG Changjiang.Low frequency oscillation mode estimation in power systems using adaptive-projection intrinsically transformed multivariate empirical mode decomposition[J].Power System Protection and Control,2020,48(14):165-174[点击复制] |
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| 摘要: |
| 传统多元经验模态分解(Multivariate Empirical Mode Decomposition, MEMD)在处理多信道量测信息时存在量测信道之间数据不平衡性及数据相关性导致的主导振荡模式辨识结果误差较大，且模式混合现象未有效消除。提出了一种基于自适应投影多元经验模态分解(Adaptive-Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition, APIT-MEMD)的电力系统主导振荡模式辨识方法。首先采用APIT-MEMD将含有振荡信息的多信道量测信息整体分解，精确分离出各量测信道内含有振荡模式的固有模态函数(Intrinsic Mode Functions, IMF)信号。然后，采用Teager能量判据甄选能表征主导振荡模式的IMF信号。进而，采用希尔伯特黄变换(Hilbert-Huang Transform, HHT)实现对各IMF中所含主导振荡模式的频率和阻尼比估计。最后，将所提方法应用到IEEE-68节点时域仿真算例和辽宁电网广域实测算例中进行分析和验证，结果表明所提方法的可行性和有效性。
关键词：自适应投影多元经验模态分解；固有模态函数；希尔伯特黄变换；振荡频率；阻尼比 |
| 关键词: 自适应投影多元经验模态分解 固有模态函数 希尔伯特黄变换 振荡频率 阻尼比 |
| DOI：10.19783/j.cnki.pspc.191009 |
| 投稿时间：2019-08-22修订日期：2019-11-14 |
| 基金项目:国家电网有限公司科技项目资助(SGTYHT17-JS- 199)；国网辽宁省电力有限公司科技项目资助(SGTYHT17JS201)“辽宁电网利用广域量测系统提升电网安全稳定运行水平的技术研究” |
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| Low frequency oscillation mode estimation in power systems using adaptive-projection intrinsically transformed multivariate empirical mode decomposition |
| ZHANG Yanjun,YIN Xiangxiang,GE Yanfeng,WEI Junhong,WANG Changjiang |
| (1. State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China;
2. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China;
3. Northeast Branch, Huadian Electric Power Research Institute Co., Ltd., Shenyang 110167, China) |
| Abstract: |
| The traditional Multivariate Empirical Mode Decomposition (MEMD) to estimate the dominant oscillation modes from measurements may have estimation errors caused by inter-channel imbalances and correlations, and mode mixing is still not effectively resolved. This paper proposes a new method based on Adaptive-Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition (APIT-MEMD) to estimate the dominant oscillation modes from the measurements in a power system. The Intrinsic Mode Functions (IMF) signals with different oscillation mode scales in each measurement channel are accurately separated by APIT-MEMD; the critical IMF associated with the dominant oscillation mode are determined by the Teager energy operator; the Hilbert-Huang Transform (HHT) is applied to identify the oscillation frequency and damping ratio of the dominant oscillation mode contained in each IMF. The method is analyzed and validated by the IEEE-68 bus test system and the PMU data collected from the Liaoning Power Grid. The results validate the feasibility and effectiveness of the proposed method.
This work is supported by Science and Technology Project of State Grid Corporation of China (No. SGTYHT17- JS-199) and Science and Technology Project of State Grid Liaoning Electric Power Co., Ltd. (No. SGTYHT17JS201). |
| Key words: adaptive-projection intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD) intrinsic mode functions Hilbert-Huang Transform (HHT) oscillation frequency damping ratio |
