| 引用本文: | 潘学萍,袁姗姗,鞠 平,卫志农.负荷随机扰动下的电力系统小波模态参数识别[J].电力自动化设备,2012,32(4): |
| PAN Xueping,YUAN Shanshan,JU Ping,WEI Zhinong.Wavelet-based electromechanical mode shape identification from ambient data of random load excitation[J].Electric Power Automation Equipment,2012,32(4): |
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| 摘要: |
| 采用小波变换方法从负荷随机扰动下的响应轨线中估算电力系统机电振荡模态。首先推导出随机激励下响应轨线的小波功率谱与系统右特征向量之间的关系，获得从响应轨线估算电力系统模态参数的小波方法，然后针对振荡频率接近的模式，提出采用小波相干系数的方法来区分其模态信息。以平稳激励下的4机2区域系统为例，仿真验证了小波方法识别随机激励下系统模态参数的有效性，与傅里叶方法相比，对于能量较弱的模式小波方法具有较好的模态辨识效果。 |
| 关键词: 电力系统 小波变换 Morlet小波 功率谱 相干函数 模式 模态 |
| DOI： |
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| 基金项目:国家自然科学基金资助项目（50877024） |
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| Wavelet-based electromechanical mode shape identification from ambient data of random load excitation |
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PAN Xueping, YUAN Shanshan, JU Ping, WEI Zhinong
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Hohai University，Nanjing 210098，China
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| Abstract: |
| The wavelet transform method is applied to identify the electromechanical mode shape properties of power system from the ambient data of random load excitation. The relationship between wavelet power spectral density and modal eigenvectors is derived and the wavelet-based coherency function is proposed to discriminate the mode shape of modes with nearly same frequency. The simulation result of a two-area four-generator power system under stationary random excitation demonstrates the effectiveness of the proposed wavelet transform method in modal parameter identification and it also shows that the wavelet method is more effective in identifying modes with weak energy. |
| Key words: electric power systems wavelet transforms Morlet wavelet power spectrum coherency function mode mode shape |
