| 引用本文: | 姜涛,刘方正,陈厚合,李雪,李国庆,葛维春.基于多通道快速傅里叶小波变换的电力系统主导振荡模式及模态协同辨识方法研究[J].电力自动化设备,2019,39(7): |
| JIANG Tao,LIU Fangzheng,CHEN Houhe,LI Xue,LI Guoqing,GE Weichun.Cooperated identification method of dominant oscillation modes and mode shapes for power system based on multi-channel fast Fourier transform based continuous wavelet transform[J].Electric Power Automation Equipment,2019,39(7): |
|
| 摘要: |
| 针对连续小波变换在主导振荡模式辨识中存在效率低的不足，提出一种快速傅里叶小波变换(CWTFT)方法以提高小波变换效率；针对单通道小波辨识的结果受振荡模式可观性影响的缺陷，提出一种多通道CWTFT，实现多通道量测信息的时频域分解，进而获得对应的小波系数矩阵；在此基础上，借助小波尺度相对能量甄别出与主导振荡模式强相关的关键小波尺度，以其为基准重构小波系数矩阵；对重构的小波系数矩阵进行奇异值分解，利用重构小波系数矩阵的第一左、右奇异特征向量辨识系统主导振荡模式及振荡模态。将所提方法应用到16机68节点测试系统和南方电网的广域实测数据中，结果验证了该方法的准确性和有效性。 |
| 关键词: 电力系统 小扰动稳定 快速傅里叶小波变换 振荡模式 振荡模态 |
| DOI：10.16081/j.issn.1006-6047.2019.07.019 |
| 分类号:TM76 |
| 基金项目:国家自然科学基金资助项目(51607034)；国家重点研发计划项目(2016YFB0900903)；国网辽宁省电力有限公司科技项目(2018YF12) |
|
| Cooperated identification method of dominant oscillation modes and mode shapes for power system based on multi-channel fast Fourier transform based continuous wavelet transform |
|
JIANG Tao1, LIU Fangzheng2, CHEN Houhe1, LI Xue1, LI Guoqing1, GE Weichun3
|
|
1.Department of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China;2.Beijing Guodian Engineering Tendering Co.,Ltd.,Beijing 100040, China;3.State Grid Liaoning Electric Power Co.,Ltd.,Shenyang 110004, China
|
| Abstract: |
| Aiming at the shortage of low efficiency of continuous wavelet transform in dominant oscillation mode identification, a CWTFT(fast Fourier Transform based Continuous Wavelet Transform) method is proposed to improve the efficiency. Since the results of single-channel wavelet identification are influenced by the observability of oscillation mode, a multi-channel CWTFT is proposed to realize time-frequency domain decomposition of measured data from multi-channel and obtain the corresponding wavelet coefficient matrix, on this basis, the relative wavelet scale energy is used to identify the critical wavelet scales strongly related to the dominant oscillation modes, with which the wavelet coefficient matrix is reconstructed. The matrix is carried out with SVD(Singular Value Decomposition),and the left and right singular eigenvectors of first singular value of the matrix are adopted to identify the dominant oscillation modes and mode shapes. The proposed method is applied in the measured data of a 16-machine 68-bus test system and China Southern Power Grid, and the results verify the correctness and effectiveness of the method. |
| Key words: electric power systems small signal stability CWTFT oscillation mode oscillation mode shape |
