基于多元经验模态分解的衰荡振荡模态识别

Shutang You, Jiahui Guo, Wenxuan Yao, Siqi Wang, Yong Liu, Yilu Liu
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引用次数: 40

摘要

大型电力系统的区域间振荡问题因其严重影响系统安全、降低输电能力而备受关注。最近大规模部署的相量测量单元(pmu)使基于在线测量的监测和分析区域间振荡模式成为可能。然而,测量结果的非平稳特性成为振荡分析的障碍。本文提出了一种多通道时频分析方法——多元经验模态分解(MEMD),用于衰荡模态识别。通过测试系统验证了MEMD在振荡模式识别方面的能力。此外,还将MEMD与经典的经验模态分解(EMD)和快速傅立叶变换(FFT)进行了比较。结果表明,MEMD可以在保留其相位和幅度信息的同时,分离不同的振荡模式,从而提高振荡识别能力。
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Ring-down oscillation mode identification using multivariate Empirical Mode Decomposition
Inter-area oscillation in a large power systems draws much attention because it might severely influence system security and reduce transmission capability. The recent large-scale deployment of phasor measurement units (PMUs) enables online measurement-based monitoring and analysis on inter-area oscillatory modes. However, the nonstationary characteristics of measurements become obstacles for oscillation analysis. This work proposes multivariate empirical mode decomposition (MEMD), a multi-channel time frequency analysis method, for ring-down oscillation mode identification. The capability of the MEMD in oscillation mode identification is verified based on a test system. In addition, MEMD is compared with classical Empirical Mode Decomposition (EMD) and Fast Fourier Transform (FFT) for evaluation. The result shows that MEMD can improve oscillation identification through separating different oscillation modes while persevering their phase and amplitude information.
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