Suzhou Electric Appliance Research Institute
期刊號: CN32-1800/TM| ISSN1007-3175

Article retrieval

文章檢索

首頁 >> 文章檢索 >> 文章瀏覽排名

基于EEMD和Prony方法的次同步振蕩分析

來源:電工電氣發(fā)布時間:2021-03-26 14:26 瀏覽次數(shù):716
基于EEMD和Prony方法的次同步振蕩分析
 
馬曉騰1,顧煜炯1,楊曉峰2
(1 華北電力大學 能源動力與機械工程學院,北京 102206;2 中國華能集團清潔能源技術(shù)研究院有限公司,北京 102209)
 
    摘 要:Prony是電力系統(tǒng)振蕩分析中常用的一種方法,但其對噪聲數(shù)據(jù)異常敏感,針對這一問題,提出基于集合經(jīng)驗?zāi)B(tài)分解(EEMD)與Prony的聯(lián)合分析方法用于分析電力系統(tǒng)次同步振蕩問題。利用EEMD對含噪聲信號進行分解,去除其中的高頻噪聲分量,同時有效解決經(jīng)驗?zāi)B(tài)分解(EMD)去噪時的模態(tài)混頻問題,得到平穩(wěn)信號后利用Prony可準確識別次同步振蕩的特征參數(shù),將該聯(lián)合分析方法用于某300 MW汽輪發(fā)電機組的次同步振蕩分析中,驗證了其抗噪性強和準確度高的優(yōu)點。
    關(guān)鍵詞:次同步振蕩;Prony方法;噪聲;集合經(jīng)驗?zāi)B(tài)分解;汽輪發(fā)電機組
    中圖分類號:TM311     文獻標識碼:A     文章編號:1007-3175(2021)03-0020-05
 
Subsynchronous Oscillation Analysis Based on Ensemble Empirical Mode Decomposition and Prony Method
 
MA Xiao-teng1, GU Yu-jiong1, YANG Xiao-feng2
(1 School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China;
2 China Huaneng Group Clean Energy Research Institute, Beijing 102209, China)
 
    Abstract: Prony method is used commonly in power system oscillation analysis, but it abnormally sensitive to noise data. A method based on ensemble empirical mode decomposition(EEMD) and Prony is proposed to solve this problem and is used to analyze subsynchronous oscillation of power system. Use EEMD to decompose the noisy signal, remove the high-frequency noise component, and effectively solve the modal mixing problem in empirical mode decomposition(EMD) denoising; after obtaining the stable signal, Prony can accurately identify the characteristic parameters of the subsynchronous oscillation. The joint analysis method is used in the subsynchronous oscillation analysis of a 300 MW steam turbine generator unit, which verifies the advantages of strong noise resistance and high accuracy.
    Key words: subsynchronous oscillation; Prony method; noise; ensemble empirical mode decomposition; steam turbine generator
 
參考文獻
[1] 金鐵錚,顧煜炯. 次同步振蕩下軸系扭振疲勞壽命損耗在線分析方法[J]. 熱力發(fā)電,2016,45(6) :100-105.
[2] 楊昆,趙鵬程,顧煜炯,等. 汽輪機扭振作用下葉片響應(yīng)及應(yīng)力計算[J]. 動力工程學報,2018,38(3) :193-197.
[3] WEN H, GUO S Y, TENG Z S, et al.Frequency Estimation of Distorted and Noisy Signals in Power Systems by FFT-Based Approach[J].IEEE Transactions on Power Systems,2014,29(2) :765-774.
[4] 馬建偉,竺煒,曾喆昭,等. FFT結(jié)合神經(jīng)網(wǎng)絡(luò)的低頻振蕩主導模式識別[J]. 電力科學與技術(shù)學報,2011,26(4) :88-93.
[5] 李天云,高磊,趙妍. 基于HHT的電力系統(tǒng)低頻振蕩分析[J]. 中國電機工程學報,2006,26(14) :24-30.
[6] 胡昊明,鄭偉,徐偉,等. Prony和HHT算法在低頻振蕩在線辨識中的適用性比較[J]. 電力系統(tǒng)保護與控制,2013,41(14) :33-40.
[7] 鄧集祥,歐小高,姚天亮. 基于小波能量系數(shù)的主導低頻振蕩模式檢測[J]. 電工技術(shù)學報,2009,24(8) :141-146.
[8] 沈楊,戴本祁,張惠東. 基于小波和擴展Prony算法的電壓閃變檢測新方法[J]. 電力系統(tǒng)保護與控制,2010,38(10) :43-47.
[9] 伍凌云,李興源,楊煜,等. 基于Prony 辨識的次同步阻尼控制器研究[J]. 電力自動化設(shè)備,2007,27(9) :12-17.
[10] 鄭蕤,肖湘寧,李偉,等. 復雜交直流系統(tǒng)次同步振蕩模態(tài)辨識及仿真驗證[J]. 高電壓技術(shù),2010,36(12) :3035-3040.
[11] 郭成,李群湛. Prony算法辨識傳遞函數(shù)的模型階數(shù)選取研究[J]. 系統(tǒng)仿真學報,2009,21(22) :7042-7044.
[12] HUANG N E, SHEN Z, LONG S R, et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society A :Mathematical, Physical and Engineering Sciences,1998(454) :903-995.
[13] FLANDRIN P, RILLING G, GONCALVES P.Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters,2004,11(2) :112-114.
[14] RILLING G, FLANDRIN P, GONCALVES P.Empirical mode decomposition, fractional Gaussian noise and Hurst exponent estimation[C]//IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
[15] BARNHART B L, EICHINGER W E.Empirical Mode Decomposition applied to solar irradiance, global temperature, sunspot number, and CO2 concentration data[J].Journal of Atmospheric and Solar- Terrestrial Physics,2011,73(13) :1771-1779.