| 引用本文: | 李国庆,王 丹,姜 涛,陈厚合.基于递归连续小波变换的电力系统振荡模式辨识[J].电力自动化设备,2016,36(12): |
| LI Guoqing,WANG Dan,JIANG Tao,CHEN Houhe.Power system oscillation mode identification based on recursive continuous wavelet transform[J].Electric Power Automation Equipment,2016,36(12): |
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| 摘要: |
| 提出一种基于递归连续小波变换的电力系统低频振荡模式辨识新方法。首先，探讨电力系统振荡模式辨识中小波系数选择的基本准则；然后，引入最小二乘支持向量机，通过对信号两端拟合延拓，消除小波变换中固有的边缘效应，改善小波辨识的准确性；在此基础上，将小波辨识与递归最小二乘相结合，递归更新振荡频率和阻尼比，实现电力系统模态参数的动态追踪。IEEE 68节点系统仿真数据和南方电网实测数据的辨识结果验证了所提方法的有效性和可行性。 |
| 关键词: 电力系统 低频振荡 连续小波变换 边缘效应 最小二乘支持向量机 递归最小二乘 |
| DOI：10.16081 ／ j.issn.1006－6047.2016.09.002 |
| 分类号: |
| 基金项目:国家自然科学基金资助项目（51377016，51477027）；长江学者和创新团队发展计划（IRT1114）；吉林省科技发展计划（20130206038GX，20140101080JC） |
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| Power system oscillation mode identification based on recursive continuous wavelet transform |
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LI Guoqing, WANG Dan, JIANG Tao, CHEN Houhe
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School of Electrical Engineering，Northeast China Dianli University，Jilin 132012，China
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| Abstract: |
| A method of low-frequency oscillation mode identification based on the recursive continuous wavelet transform is proposed for power system. The fundamental principles of wavelet coefficient selection for the oscillation mode identification of power system are discussed. The least squares support vector machine is applied to fit and extent both ends of signal data for eliminating the inherit edge effect of wavelet transform and improving the accuracy of wavelet identification. The wavelet identification is combined with the recursive least squares to recursively update the oscillation frequency and damping ratio for the dynamic tracking of modal parameters of power system. The simulative data of IEEE 68-bus system and the measured data of China Southern Power Grid verify the effectiveness and feasibility of the proposed method. |
| Key words: electric power systems low-frequency oscillation continuous wavelet transform edge effect least squares support vector machine recursive least squares |
