| 引用本文: | 苏安龙,孙志鑫,何晓洋,张艳军,王长江.基于多元经验模式分解的电力系统低频振荡模式辨识[J].电力系统保护与控制,2019,47(22):113-125.[点击复制] |
| SU Anlong,SUN Zhixin,HE Xiaoyang,ZHANG Yanjun,WANG Changjiang.Identification of power system low frequency oscillation mode based on multivariate empirical mode decomposition[J].Power System Protection and Control,2019,47(22):113-125[点击复制] |
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| 摘要: |
| 提出了一种辨识电力系统主导低频振荡模式的新方法。该方法结合了多元经验模式分解(Multivariate Empirical Mode Decomposition, MEMD)、Teager能量算子及预测误差法(Prediction Error Method，PEM)，通过多元经验模式分解将含电力系统低频振荡特征信息的信号进行分解，得到多个本征模函数(Intrinsic Mode Function, IMF)分量；借助Teager能量算子的快速响应能力，筛选出含有主导振荡模式的主要IMF分量；最后采用预测误差法辨识出各主导振荡模式的振荡频率和阻尼。分别利用IEEE68节点测试系统和辽宁电网实测PMU数据对所提方法进行分析、验证。结果表明，该方法可有效从电力系统的广域量测信息中辨识出电力系统的主导振荡模式。 |
| 关键词: 电力系统 低频振荡 多元经验模式分解 Teager能量算子 预测误差法 |
| DOI：10.19783/j.cnki.pspc.181554 |
| 投稿时间：2018-12-04修订日期：2019-01-23 |
| 基金项目:国网辽宁省电力有限公司科技项目(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”;国家电网公司总部科技项目资助(SGTYHT 17JS199)“千万千瓦级分层接入直流送受端系统动态行为机理和协调控制措施” |
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| Identification of power system low frequency oscillation mode based on multivariate empirical mode decomposition |
| SU Anlong,SUN Zhixin,HE Xiaoyang,ZHANG Yanjun,WANG Changjiang |
| (State Grid Liaoning Electric Power Supply CO., LTD., Shenyang 110006, China;School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China) |
| Abstract: |
| A new method about dominant low frequency oscillation mode identification for power system is proposed. The method combines Multivariate Empirical Mode Decomposition (MEMD), Teager energy operator and Prediction Error Method (PEM). The signal containing low frequency oscillation characteristic information of power system is decomposed by MEMD into several Intrinsic Mode Functions (IMFs) components. The main IMF containing dominant oscillation mode is selected by means of the rapid response ability of Teager energy operator. The Prediction Error Method (PEM) is applied to identify the oscillation frequency and damping of each dominant oscillation mode. The method is validated by IEEE68 bus test system and the PMU data of the Liaoning Power Grid. The analysis results demonstrate that this method can effectively identify the dominant oscillation mode from the wide-area measurement information of power system. This work is supported by Science and Technology Project of State Grid Liaoning Electric Power Company (No. SGTYHT17JS201) and Science and Technology Project of State Grid Corporation of China (No. SGTYHT17JS199). |
| Key words: power system low frequency oscillation multivariate empirical mode decomposition Teager energy operator prediction error method |
