基于类噪声小波分解的风电场次同步振荡辨识
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摘要
类噪声信号是电力系统正常运行过程中被噪声信号所掩盖的小扰动响应。基于风电场运行过程中类噪声信号,提出了一种风电场次同步振荡辨识方法。首先,将采样信号通过小波方法分解为多个不同频段的小波分量,基于次同步振荡频率的范围选取响应频段的小波分量;然后将信号重构获得次同步频段的信号;最后通过对重构信号的辨识,获得次同步振荡的频率与阻尼系数,同时通过计算进一步获得系统串补度。仿真结果证明,所提方法能够有效提取类噪声信号中的有效信息,能够准确获取线路参数及风电场次同步振荡模态,从而有助于对风电场潜在的次同步振荡风险进行预警,以及时调整风电场的运行方式。
In power system, the responses of small signal disturbances are always covered under ambient noises. In this paper, a wind farm sub-synchronous oscillation mode identification method is proposed based on noise-like signals and wavelet decomposition. Firstly, based on wavelet decomposition, the noise-like signal is decomposed into components in different frequency bands. Then, the component signals higher or lower than the sub-synchronous frequency are filtered out while the rest component signals in subsynchronous frequency band are reconstructed. Finally, by mode identification of the new signal, the potential subsynchronous oscillation modes and their parameters are identified. The compensation level of the series compensation capacitor can be calculated. The mode identification result can be used for wind farm sub-synchronous oscillation early warning and dynamic stability analysis. Simulation result verifies effectiveness of the proposed method.
In power system, the responses of small signal disturbances are always covered under ambient noises. In this paper, a wind farm sub-synchronous oscillation mode identification method is proposed based on noise-like signals and wavelet decomposition. Firstly, based on wavelet decomposition, the noise-like signal is decomposed into components in different frequency bands. Then, the component signals higher or lower than the sub-synchronous frequency are filtered out while the rest component signals in subsynchronous frequency band are reconstructed. Finally, by mode identification of the new signal, the potential subsynchronous oscillation modes and their parameters are identified. The compensation level of the series compensation capacitor can be calculated. The mode identification result can be used for wind farm sub-synchronous oscillation early warning and dynamic stability analysis. Simulation result verifies effectiveness of the proposed method.
引文
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[17]赵妍,李武璟,聂永辉.次同步振荡的频率切片小波变换检测方法.电工技术学报,2017,32(6):106-114.Zhao Yan,Li Wujing,Nie Yonghui.A method for subsynchronous oscillation detectionusing frequencyslicewavelet transform[J].Transactions of China Electrotechnical Society,2017,32(6):106 -114(in Chinese).
[18]刘海洋,李兴源,徐大鹏.基于小波分析的次同步振荡检测研究[J].四川电力技术,2008,31(1):5-7.LiuHaiyang,LiXingyuan,XuDapeng.Waveletanalysisbased subsynchronousoscillationdetection[J]. SichuanElectricPower Technology,2008,31(1):5-7(in Chinese).
[19]王东阳,晁晖,王辛,等.基于小波Mallat算法的次同步振荡监测[J].智慧电力,2011,39(8):37-41.Wang Dongyang,Chao Hui,Wang Xin,et al.Mallet algorithm of waveletanalysisbasedSSOmonitoring[J].SmartPower,2011,39(8):37-41(in Chinese).
[20]Rueda J L,Juarez C A,Erlich I.Wavelet-based analysis of power systemlow-frequency electromechanicaloscillations[J]. IEEE Transactions on Power Systems,2011,26(3):1733-1743.
[21]WickerhauserMV. Adaptedwaveletanalysisfromtheoryto software[M].A. K. Peters/CRC Press,1996.
[22]刘思议,金涛,刘对.基于改进小波阈值去噪和RCRSV-MP算法的电力系统低频振荡模态辨识[J].电力自动化设备,2017,37(8):166-172.Liu Siyi,Jin Tao,Liu Dui.Power system low-frequency oscillation modeidentificationbaseonimprovedwaveletthresholdde-noising andRCRSV-MPalgorithm[J]. ElectricPowerAutomation Equipment,2017,37(8):166-172(in Chinese).
[23]Ma J,Jiang L,Wu M,et al.SSR analysis of DFIG based wind farm consideringspatial distribution of windspeed[C]//Proceedingsof IEEE Power and Energy Society General Meeting.Boston,USA:IEEE,2016:1-5.
[2]Feng Y,Lin H,Ho S.Overview of wind power generation in China:status and development[J]. Renewable and Sustainable Energy Reviews,2015(50):847-858.
[3]Xie H,Li B,Heyman C.Subsynchronous resonance characteristics in presence of doubly-fed induction generator and series compensation andmitigationofsubsynchronousresonancebypropercontrolof seriescapacitor[J]. IETTransactionsonRenewablePower Generation,2014,8(4):411-421.
[4]李明节,于钊,许涛,等.新能0源?并网系统引发的复杂振荡问题及其对策研究[J],电网技术,2017,41(4):1035-1042.Li Mingjie,Yu Zhao,Xu Tao,et al.Study of complex oscillation causedbyrenewableenergyintegrationanditssolution[J].Power System Technology,2017,41(4):1035-1042(in Chinese).
[5]王伟胜,张冲,何国庆,等.大规模风电场并网系统次同步振荡研究综述[J].电网技术,2017,41(4):1050-1060.WangWeisheng,ZhangChong,HeGuoqing,etal.Overviewof researchonsubsynchronousoscillationsinlarge-scalewindfarm integrated system[J].Power System Technology,2017,41(4):1050-1060(in Chinese).
[6]Suriyaarachchi D H R,Annakkage U D,Karawita C.A procedure to study sub-synchronous interactions in wind integrated power systems[J].IEEE Transactions on Power Systems,2013,28(1):377-384.
[7]Wang L,Xie X,Jiang Q.Investigation of SSR in practical DFIGbasedwindfarmsconnectedtoaseries-compensatedpowersystem[J].IEEE Transactions on Power Systems,2015,30(5):2772-2779.
[8]吴超.电力系统模型类噪声闭环辨识方法[J].电力系统自动化,2013,37(7):31-35.Wu Chao.An ambient data based closed-loop identification method of powersystem[J].AutomationofElectricPowerSystems,2013,37(7):31-35(in Chinese).
[9]吴超,陆超,韩英铎,等.基于类噪声信号和ARMA-P方法的振荡模态辨识[J].电力系统自动化,2010,34(6):1-6.Wu Chao,Lu Chao,Han Yingduo,et al.Identification of mode shape basedonambientsignalsandARMA-Pmethod[J].Automationof Electric Power Systems,2010,34(6):1-6(in Chinese).
[10]李安娜,吴熙,蒋平,等.基于形态滤波和Prony算法的低频振荡模式辨识的研究[J].电力系统保护与控制,2015,43(3):137-142.LiAnna,WuXi,JiangPing,etal.Researchonidentifyinglow frequencyoscillationmodesbasedonmorphologicalfilteringtheory andPronyalgorithm[J].PowerSystemProtectionandControl,2015,43(3):137-142(in Chinese).
[11]张程,金涛.基于ISPM和SDM-Prony算法的电力系统低频振荡模式辨识[J].电网技术,2016,40(4):1209-1216.Zhang Cheng,Jin Tao.Identification of power system low frequency oscillationswithISPMandSDMSDM-Prony[J].PowerSystem Technology,2016,40(4):1209-1216(in Chinese).
[12]谢敏,周凯,何珉,等.史坦无偏估计自适应奇异值分解在局放信号白噪声抑制中的应用[J].电网技术,2018,42(12):9-17.Xie Min,Zhou Kai,He Min,et al.Application of adaptive singular value decomposition based on stein unbiased risk estimation in partial dischargesignalwhitenoisesuppression[J]. PowerSystem Technology,2018,42(12):9-17(in Chinese).
[13]Xiao H,Wei J,Liu H.An identification method for power system low frequencyoscillationsbasedonimprovedVMDandTKEO[J].IET Generation Transmission and Distribution,2017,11(16):4096-4103.
[14]李宽,李兴源,赵睿,等.基于希尔伯特-黄变换的次同步振荡检测研究[J].华东电力,2012,40(1):62-66.Li Kuan,Li Xingyuan,Zhao Rui,et al.Subsynchronous oscillation detectionbasedonHilbert-Huangtransformmethod[J].EastChina Electric Power,2012,40(1):62-66(in Chinese).
[15]董飞飞,刘涤尘,涂炼,等.基于MM-ARMA算法的次同步振荡模态参数辨识[J].高电压技术,2013,39(5):1252-1257.Dong Feifei,Liu Dichen,Tu Lian,et al.Subsynchronous oscillation modeparameteridentificationbasedonMM-ARMAalgorithm[J].High Voltage Engineering,2013,39(5):1252-1257(in Chinese).
[16]张昊,石铁洪,刘沛.基于双正交小波和复小波的次同步谐振检测方法[J].中国电机工程学报,2001,21(3):12-15.ZhangHao, ShiTiehong, LiuPei. Thedetectionoftorsional oscillationbasedonthebi-orthogonalwaveletandcomplexwavelet[J].Proceedings of the CSEE,2001,21(3):12-15(in Chinese).
[17]赵妍,李武璟,聂永辉.次同步振荡的频率切片小波变换检测方法.电工技术学报,2017,32(6):106-114.Zhao Yan,Li Wujing,Nie Yonghui.A method for subsynchronous oscillation detectionusing frequencyslicewavelet transform[J].Transactions of China Electrotechnical Society,2017,32(6):106 -114(in Chinese).
[18]刘海洋,李兴源,徐大鹏.基于小波分析的次同步振荡检测研究[J].四川电力技术,2008,31(1):5-7.LiuHaiyang,LiXingyuan,XuDapeng.Waveletanalysisbased subsynchronousoscillationdetection[J]. SichuanElectricPower Technology,2008,31(1):5-7(in Chinese).
[19]王东阳,晁晖,王辛,等.基于小波Mallat算法的次同步振荡监测[J].智慧电力,2011,39(8):37-41.Wang Dongyang,Chao Hui,Wang Xin,et al.Mallet algorithm of waveletanalysisbasedSSOmonitoring[J].SmartPower,2011,39(8):37-41(in Chinese).
[20]Rueda J L,Juarez C A,Erlich I.Wavelet-based analysis of power systemlow-frequency electromechanicaloscillations[J]. IEEE Transactions on Power Systems,2011,26(3):1733-1743.
[21]WickerhauserMV. Adaptedwaveletanalysisfromtheoryto software[M].A. K. Peters/CRC Press,1996.
[22]刘思议,金涛,刘对.基于改进小波阈值去噪和RCRSV-MP算法的电力系统低频振荡模态辨识[J].电力自动化设备,2017,37(8):166-172.Liu Siyi,Jin Tao,Liu Dui.Power system low-frequency oscillation modeidentificationbaseonimprovedwaveletthresholdde-noising andRCRSV-MPalgorithm[J]. ElectricPowerAutomation Equipment,2017,37(8):166-172(in Chinese).
[23]Ma J,Jiang L,Wu M,et al.SSR analysis of DFIG based wind farm consideringspatial distribution of windspeed[C]//Proceedingsof IEEE Power and Energy Society General Meeting.Boston,USA:IEEE,2016:1-5.