风电机组短期可靠性预测模型与风电场有功功率控制策略研究
|
摘要
风能作为一种成熟的可再生能源技术，近年来在世界范围内得到快速发展。然而，由于长期处于恶劣的自然环境中，风电机组的停运率较高，降低了风电场的运行经济性。同时，在电网的约束下，风电场也会因为风电功率预测误差和风电机组强迫停运产生一定程度的发电量损失。本文以提高风电场运行经济性为目标，对风速与风电功率预测误差分布特性、风电机组状态参数异常辨识方法、风电机组短期可靠性预测方法以及风电场有功功率控制策略进行了研究。论文主要包括以下内容：
①提出了风速与风电功率预测误差的核密度分布模型，研究了预测误差分布特性。采用多种典型方法进行风速预测，对比分析了单机风速和风电场风速的预测精度。建立了基于实测数据的功率曲线，结合风速预测方法进行了风电功率预测。研究了单机风速与风电功率预测误差分布与风速、预测时间间隔之间的关系。结果表明，在三种预测方法下，单机风速的预测误差均明显大于风电场风速的预测误差，不同风速区间内的预测误差分布差异明显，预测误差随预测时间间隔的增大而增大。
②提出并建立了风电机组状态参数广义模糊异常辨识模型。研究了风电场SCADA系统提供的风电机组状态参数与自然环境和机组工作特征的关联关系，建立了状态参数的预测模型，分析了影响预测模型精度的主要因素。对比了本机近期数据模型、本机历史数据模型和其他机组近期数据模型这三类预测模型的预测精度，在此基础上提出了预测模型的选择方法和预测残差的异常程度量化方法，最终采用模糊综合评判进行状态参数的异常辨识。结果表明，广义模糊异常辨识模型综合了多个预测模型的异常辨识结果，具有更高的准确度。
③提出了风电机组短期可靠性预测模型。计及风速与风电机组停运率的相关性，建立了考虑风速的风电机组统计停运模型。对与自然环境密切相关的状态参数，提出了基于状态参数概率预测的保护动作模型；对具有保护动作整定时间的状态参数，提出了基于越限时间的保护动作模型。最后，综合考虑各状态参数的越限保护动作概率和统计停运概率，提出了计及状态参数越限的风电机组短期可靠性预测模型。结果表明，通过对状态参数越限概率的计算，风电机组短期停运模型的准确性得到大幅提高。
④提出了考虑机组短期可靠性和功率预测误差的有功功率控制方法。分析了风电场在无电网约束和限功率运行两种情况下的发电量损失原因，在无电网约束情况下，通过对低可靠性机组进行降功率控制以减小其短期停运概率。针对风电场限功率运行情况，提出采用功率损失风险对可能损失的功率进行量化，并提出了多台机组总功率损失风险的蒙特卡洛模拟计算方法，获取了单机功率损失风险和风电场总功率损失风险的关系，提出了限功率情况下的风电场有功功率协调控制策略。结果表明，本文提出的方法有效降低了风电场的发电量损失。
上述工作是为解决目前风电场运行维护成本偏高的问题所做的积极探索，不仅丰富了相关领域的研究成果，而且为大规模风电的安全高效利用提供了解决方案。
As one of the most important and mature renewable energy,wind energy developedrapidly in the word range in recent years. However, economic operation of wind farmsis affected by the reliabilityof WTs and constraint of power grid. On one hand, theoutage rate of wind turbines is much higher than tranditional electrical components dueto the harsh natural environment. On the other hand, part of energy loss under theconstraint of power grid due to the error of wind speed prediction and WT outages.Although related technologies are developed in recent years, there are still manyproblems to be solved. The distribution of forecast error of wind speed and wind power,anomaly identification method of condition monitoring parameters of wind turbines,short-term reliability assessment method for wind turbines, and the economicdispatching method of wind turbines in wind farms are presented in this thesis. Themain contents are shown as follows:
①A Kernel density distribution model of forecasting error was proposed, and therelationship between among forecasting error distribution characteristics,wind speedand forecastingtime interval was studied. A variety of typical methods were utilized toforecast wind speed and the forecasting accuracy of single wind turbinewas analyzed incomparison with that of wind farms. Then a power curve based on actual measurementwas established and wind power was forecasted with the wind speed forecastingmethods. Results show that with three forecasting methods the forecasting error in thesingle wind turbine level is significantly greater than that in the wind farm level; theforecasting error increases when the forecasting time interval increases; the differenceamong forecasting error distributions in different wind speed ranges is quite significant.
②A generalized fuzzy abnormal identification model for wind turbines wasproposed. The correlation relationship between wind turbine parameters provided by theSCADA system and the natural environment or the wind turbine operationcharacteristics was studied. Then a forecasting model for parameters was establishedand the main factors influencing the accuracy were analyzed. The forecasting accuracyof the native recent data model of the local wind turbines, the history data model of thelocal wind turbines and the recent data modelof other wind turbines were analyzed andcompared to each other. A selection method for the forecasting model and a quantitativemethod for the degree of abnormality of forecasting residual error were put forward. Finally, the fuzzy comprehensive evaluation was utilized to identify the abnormalparameters. The results show that theaccuracy of identifying abnormal parameters ishigher when the identifying results of abnormal state of multiple forecasting models aretaken into account.
③Considering the correlation between wind speed and wind turbine outage rate,a wind turbine outage model with the consideration of wind speed was established. Forthe parameters closely related to the natural environment, an off-limitprotectionactionmodel based on parameters probability forecastwas presented.Accordingly, for the parameters which have setting time of protection action, anoff-limit protectionactionmodelbased on the time of off-limit was present. Finally,considering the limit protection probability and the statistical outage probability of eachstate parameter comprehensively, a wind turbine reliability evaluation modelconsidering the parameters limit was put forward. The results show that the accuracy ofthe short-termoutage model for the wind turbine model is much higher by calculatingthe parameters off-limit probability.
④An effective controlling method for active power was presented consideringpower forecasting error andshort-term reliability of wind turbines. Without the gridconstrains, the short-term outage probability can be reduced by implementing thepower-reduced control measures on the wind turbines with low reliability. For thelimited power operating condition, the risk of power loss was proposed to quantifypossible power loss. Finally, based on the concept of monte carlo simulation, thecalculating method for the risk of multiple wind turbines power loss was presented, thusthe relation between the risk of single wind power loss and the wind farm total powerloss was obtained. Besides, the coordinated control strategy of wind farm active powerunder limited power conditionwas presented. The results show that the methodsmentioned in this thesis effectively reduce the power loss of wind farms.
The above work is active exploration for reducing the high cost of operation andmaintenance of wind farm. The methods proposed in this thesis not only enrich researchin related fields, but also offer feasible solution to efficient use of large-scale windpower.
①提出了风速与风电功率预测误差的核密度分布模型，研究了预测误差分布特性。采用多种典型方法进行风速预测，对比分析了单机风速和风电场风速的预测精度。建立了基于实测数据的功率曲线，结合风速预测方法进行了风电功率预测。研究了单机风速与风电功率预测误差分布与风速、预测时间间隔之间的关系。结果表明，在三种预测方法下，单机风速的预测误差均明显大于风电场风速的预测误差，不同风速区间内的预测误差分布差异明显，预测误差随预测时间间隔的增大而增大。
②提出并建立了风电机组状态参数广义模糊异常辨识模型。研究了风电场SCADA系统提供的风电机组状态参数与自然环境和机组工作特征的关联关系，建立了状态参数的预测模型，分析了影响预测模型精度的主要因素。对比了本机近期数据模型、本机历史数据模型和其他机组近期数据模型这三类预测模型的预测精度，在此基础上提出了预测模型的选择方法和预测残差的异常程度量化方法，最终采用模糊综合评判进行状态参数的异常辨识。结果表明，广义模糊异常辨识模型综合了多个预测模型的异常辨识结果，具有更高的准确度。
③提出了风电机组短期可靠性预测模型。计及风速与风电机组停运率的相关性，建立了考虑风速的风电机组统计停运模型。对与自然环境密切相关的状态参数，提出了基于状态参数概率预测的保护动作模型；对具有保护动作整定时间的状态参数，提出了基于越限时间的保护动作模型。最后，综合考虑各状态参数的越限保护动作概率和统计停运概率，提出了计及状态参数越限的风电机组短期可靠性预测模型。结果表明，通过对状态参数越限概率的计算，风电机组短期停运模型的准确性得到大幅提高。
④提出了考虑机组短期可靠性和功率预测误差的有功功率控制方法。分析了风电场在无电网约束和限功率运行两种情况下的发电量损失原因，在无电网约束情况下，通过对低可靠性机组进行降功率控制以减小其短期停运概率。针对风电场限功率运行情况，提出采用功率损失风险对可能损失的功率进行量化，并提出了多台机组总功率损失风险的蒙特卡洛模拟计算方法，获取了单机功率损失风险和风电场总功率损失风险的关系，提出了限功率情况下的风电场有功功率协调控制策略。结果表明，本文提出的方法有效降低了风电场的发电量损失。
上述工作是为解决目前风电场运行维护成本偏高的问题所做的积极探索，不仅丰富了相关领域的研究成果，而且为大规模风电的安全高效利用提供了解决方案。
As one of the most important and mature renewable energy,wind energy developedrapidly in the word range in recent years. However, economic operation of wind farmsis affected by the reliabilityof WTs and constraint of power grid. On one hand, theoutage rate of wind turbines is much higher than tranditional electrical components dueto the harsh natural environment. On the other hand, part of energy loss under theconstraint of power grid due to the error of wind speed prediction and WT outages.Although related technologies are developed in recent years, there are still manyproblems to be solved. The distribution of forecast error of wind speed and wind power,anomaly identification method of condition monitoring parameters of wind turbines,short-term reliability assessment method for wind turbines, and the economicdispatching method of wind turbines in wind farms are presented in this thesis. Themain contents are shown as follows:
①A Kernel density distribution model of forecasting error was proposed, and therelationship between among forecasting error distribution characteristics,wind speedand forecastingtime interval was studied. A variety of typical methods were utilized toforecast wind speed and the forecasting accuracy of single wind turbinewas analyzed incomparison with that of wind farms. Then a power curve based on actual measurementwas established and wind power was forecasted with the wind speed forecastingmethods. Results show that with three forecasting methods the forecasting error in thesingle wind turbine level is significantly greater than that in the wind farm level; theforecasting error increases when the forecasting time interval increases; the differenceamong forecasting error distributions in different wind speed ranges is quite significant.
②A generalized fuzzy abnormal identification model for wind turbines wasproposed. The correlation relationship between wind turbine parameters provided by theSCADA system and the natural environment or the wind turbine operationcharacteristics was studied. Then a forecasting model for parameters was establishedand the main factors influencing the accuracy were analyzed. The forecasting accuracyof the native recent data model of the local wind turbines, the history data model of thelocal wind turbines and the recent data modelof other wind turbines were analyzed andcompared to each other. A selection method for the forecasting model and a quantitativemethod for the degree of abnormality of forecasting residual error were put forward. Finally, the fuzzy comprehensive evaluation was utilized to identify the abnormalparameters. The results show that theaccuracy of identifying abnormal parameters ishigher when the identifying results of abnormal state of multiple forecasting models aretaken into account.
③Considering the correlation between wind speed and wind turbine outage rate,a wind turbine outage model with the consideration of wind speed was established. Forthe parameters closely related to the natural environment, an off-limitprotectionactionmodel based on parameters probability forecastwas presented.Accordingly, for the parameters which have setting time of protection action, anoff-limit protectionactionmodelbased on the time of off-limit was present. Finally,considering the limit protection probability and the statistical outage probability of eachstate parameter comprehensively, a wind turbine reliability evaluation modelconsidering the parameters limit was put forward. The results show that the accuracy ofthe short-termoutage model for the wind turbine model is much higher by calculatingthe parameters off-limit probability.
④An effective controlling method for active power was presented consideringpower forecasting error andshort-term reliability of wind turbines. Without the gridconstrains, the short-term outage probability can be reduced by implementing thepower-reduced control measures on the wind turbines with low reliability. For thelimited power operating condition, the risk of power loss was proposed to quantifypossible power loss. Finally, based on the concept of monte carlo simulation, thecalculating method for the risk of multiple wind turbines power loss was presented, thusthe relation between the risk of single wind power loss and the wind farm total powerloss was obtained. Besides, the coordinated control strategy of wind farm active powerunder limited power conditionwas presented. The results show that the methodsmentioned in this thesis effectively reduce the power loss of wind farms.
The above work is active exploration for reducing the high cost of operation andmaintenance of wind farm. The methods proposed in this thesis not only enrich researchin related fields, but also offer feasible solution to efficient use of large-scale windpower.
引文
[1] Global Wind Energy Council (GWEC). Global Wind Report2012[R].2012.
[2]国家发改委能源所和IEA.中国风电发展路线图2050[R].2011.
[3]蒋泽甫.风电转换系统可靠性评估及其薄弱环节辨识[D].重庆:重庆大学博士学位论文.2012.
[4]杨校生.风力发电技术与风电场工程[M].北京:化学工业出版社,2011.
[5] Tavner P.J., Spinato F., van Bussel G.J.W., Koutoulakos E.. Reliability of different windturbine concepts with relevance to offshore application[C]. European Wind Energy Conference,Brussels, Belgium,2008.
[6] D. McMillan, G.W. Ault. Condition monitoring benefit for onshore wind turbines: sensitivityto operational parameters[J]. IET Renew. Power Gener.,2008,2(1):60–72.
[7] The European Wind Energy Association. Wind power economics[R].2010.
[8] Tavner P.J., J. Xiang, F. Spinato. Reliability analysis for wind turbines[J]. Wind Energy,2007,10:1-8.
[9] F. Spinato1, P.J. Tavner1, G.J.W. van Bussel, E. Koutoulakos. Reliability of wind turbinesubassemblies[J]. IET Renew. Power Gener.,2009,3(4):387-401.
[10] Felanalys, Database of failures for Swedish wind power turbines1997–2005[DB],2007.
[11] Elforsk N.E. Carlstedt, C. Szadkowski, C. Karlstr¨om Elforskrapporter. Performance of windpower plants, annual report,1997–2004[EB/OL]．http://www.elforsk.se
[12] H. Holttinen, T. Lakso, and M. Marjaniemi. Performance of wind power plants, annual report,2000–2005[EB/OL]. http://www.vtt.fi
[13] P J Tavner, R Gindele, S Faulstich. Study of effects of weather&location on wind turbinefailure rates[C]. European Wind Energy Conference, Warsaw, Poland,2010.
[14] Kaigui Xie, Zefu Jiang, Wenyuan Li. Effect of wind speed on wind turbine power converterreliability[J]. IEEE Trans. Energy Convers.,2012,27(1):96-104.
[15]蒋程,张建华,刘先正,于坤山.计及运行工况的风电机组停运模型[J],电力系统保护与控制,2013,41(24):112-116.
[16]高俊峰.中国风电发展报告2012[M].北京:中国环境科学出版社.2012.
[17]中华人民共和国国家质量监督检验检疫总局,中华人民共和国国家标准GB/T19963-2011,风电场接入电力系统技术规定[S].
[18]国家电网公司,风电场接入电网技术规定实施细则[S].
[19] Ghoshal A, Sundaresan MJ, Schulz MJ, Pai PF. Structural health monitoring techniques forwind turbines blades[J]. J. Wind Eng. Ind. Aerodyn.,2000,85:309-324.
[20] Cozens NJ, Watson SJ. State of the art condition monitoring techniques suitable for windturbines and wind farm applications[R]. Report for CONMOW project;2003.
[21] Caselitz P, Giebhardt J. Fault prediction techniques for offshore wind farm maintenance andrepair strategies[C]. Annual Conference of the IEEE Industrial Electronics Society, Vienna,Austria,2003.
[22] Caselitz P, Giebhardt J, Mevenkamp M. On-line fault detection and prediction in wind energyconverters[C]. Annual Conference of the IEEE Industrial Electronics Society, Thessaloniki,Greece,1994:623-627.
[23] Futter DN. Vibration monitoring of industrial gearboxes using time domain averaging[C].IMechE2nd International conference on gearbox noise, vibration and diagnostics,1995.
[24] Hong J-C, Kim YY, Lee HC, Lee YW. Damage detection using the Lipschitz exponentestimated by the wavelet transform: application to vibration modes of a Beam[J]. Int. J. SolidsStruct.,2002,39:1803-1816.
[25] Sun Q, Tang Y, Yang W, Ji Y. Feature extraction with discrete wavelet transform for drill wearmonitoring[J]. J. Vib. Control,2005,11:1375-1396.
[26] Nikolaou NG, Antoniadis IA. Rolling element bearing fault diagnosis using wavelet packets[J].NDT E Int.,2002,35(3):197-205.
[27] Ziola S．Digital signal processing of modal emission signals[J]. J. Acoust. Emiss.,1996,14:12-18．
[28] Tandon N, Nakra BC. Defect detection in rolling element bearings by acoustic emissionmethod[J]. J. Acoust. Emiss.,1990,9(1):25-28.
[29] Tan CC. Application of acoustic emission to the detection of bearing failures[C]. TribologyConference, Brisbane,1990:110-114.
[30] Blanch MJ, Dutton AG. Acoustic emission monitoring of field Tests of an operating windturbine[J]. Key Eng. Mat.,2003,245:475-482.
[31] Jungert A. Damage detection in wind turbine blades using two different acoustic techniques[J].The e-Journal of Nondestructive Testing,2008,25.
[32]陈长征,赵新光,周勃,谷泉.风电机组叶片裂纹故障特征提取方法[J].中国电机工程学报,2013,33(2):112-118.
[33] Juengert A, Grosse CU. Inspection techniques for wind turbine blades using ultrasound andsound waves[C]. Non-destructive testing in Civil Engineering, Nantes, France,2009.
[34] Jasiuniene E, Raisutis R, Sliteris R, Voleisis A, Vladisauskas A, Mitchard D. NDT of windturbine blades using adapted ultrasonic and radiographic techniques[J].Insight-Non-Destructive Testing and Condition Monitoring,2009,51(9):477-483.
[35] Schoen RR, Lin BK, Habetler TG, Schlag JH, Farag S. An unsupervised, on-line system forinduction motor fault detection using stator current monitoring[J]. IEEE Trans. Ind. Appl.,1995,31(6):1280-1286.
[36] Wenxian Yang, Peter J. Tavner, Christopher J. Crabtree, Michael Wilkinson. Cost-EffectiveCondition Monitoring for Wind Turbines[J]. IEEE Trans. Ind. Electron.,2010,57(1):263-271.
[37]龙泉,刘永前,杨勇平.状态监测与故障诊断在风电机组上的应用[J].现代电力,2008,25(6):55-59.
[38] Schroeder K, Ecke W, Apitz J, Lembke E, Lenschow G. A fibre Bragg grating sensor systemmonitors operational load in a wind turbine rotor blade[J]. Meas. Sci. Technol.,2006,17(5):1167-1172.
[39] Combet L, Gelman L. An automated methodology for performing time synchronous averagingof a gearbox signal without speed sensor[J]. Mech. Syst. Signal Process,2007,21(6):2590-2606.
[40] Caselitz P, Giebhargt J, Kruger T, Mevenkamp M. Development of a Fault Detection Systemfor Wind Energy Converters[C]. Proceedings of the EUWEC’96, Gotegorg,1996:1004-1007.
[41] Caselitz P, Giebhargt J, Rotor condition monitoring for improved operational safety of offshorewind energy converters[J]. J. Sol. Energy Eng. Trans. ASME,127:253-261.
[42] Staszewski WJ, Tomlinson GR. Application of the wavelet transform to fault detection in aSpur gear[J]. Mech. Syst. Signal Process,1994,8:289-307.
[43] Wang WJ, McFadden PD. Application of wavelets to gearbox vibration signals for faultdetection[J]. J. Sound Vib.,1996,192:927-939.
[44] Luo GY, Osypiw D, Irle M. On-Line vibration analysis with fast continuous wavelet algorithmfor condition monitoring of bearing[J]. J. Vib. Control,2003,9:931-947.
[45] Rubini R, Meneghetti U. Application of the envelope and wavelet transform analyses for thediagnosis of incipient faults in ball bearings[J]. Mech. Syst. Signal Process,2001,15:287-302.
[46] Dawei Xiang, Li Ran, Peter Tavner, Angus Bryant, Shaoyong Yang, Philip Mawby. MonitoringSolder Fatigue in a Power Module Using Case-Above-Ambient Temperature Rise[J]. IEEETrans. Ind. Appl.,2011,47(6):2578-2591.
[47]郭鹏,徐明,白楠,马登昌.基于SCADA运行数据的风电机组塔架振动建模与监测[J].中国电机工程学报,2013,33(5):128-136.
[48]郭鹏, David Infield,杨锡运.风电机组齿轮箱温度趋势状态监测及分析方法[J].中国电机工程学报,2013,31(32):129-136.
[49] Meik Schlechtingen, Ilmar Ferreira Santos. Comparative analysis of neural network andregression based condition monitoring approaches for wind turbine fault detection[J]. Mech.Syst. Signal Process,2011,25(5):1849-1875.
[50] Andrew Kusiak, Anoop Verma. Wind turbine condition monitoring based on SCADA datausing normal behavior models. Part1: System description[J]. Appl. Soft Comput. J.,2014,14:447-460.
[51] Andrew Kusiak, Anoop Verma. Analyzing bearing faults in wind turbines: A data-miningapproach[J]. Renew. Energy,2012,48:110-116.
[52]李辉,胡姚刚,唐显虎,刘志详.并网风电机组在线运行状态评估方法[J].中国电机工程学报,2010,30(33):103-109.
[53]李辉,胡姚刚,杨超,李学伟,唐显虎.并网风电机组运行状态的物元评估方法[J].电力系统自动化,2011,35(6):81-85.
[54]董玉亮,李亚琼,曹海斌,何成兵,顾煜炯.基于运行工况辨识的风电机组健康状态实时评价方法[J].2013,33(11):88-95.
[55] Wegley H, Formica W. Test applications of a semi-objective approach to wind forecasting forwind forecasting for wind energy applictions[R]. PNL-4403, Pacific Northwest Laboratory,1983.
[56] Troen I, Landberg L. Short term predietion of local wind conditions[C]. EuroPean CommunityWind Energy Conference, Madrid, Spain,1990:76-78.
[57] Landberg L. A mathematical look at a Physical Power Predietion model[J]. Wind Energy,1998,l:23-28.
[58] Watson R, Landberg L, Costello R, McCoy D, O’Donnell P. Evaluation of the Prediktor windpower forecasting system in Ireland[C]. European wind energy Conference, Copenhagen,2001.
[59] G. Giebel, L. Landberg, T.S. Nielsen. The Zephyr-Project: the next generation predictionsystem[C]. Poster P_GWP145on the Global Wind power conference and Exhibition,2002.
[60] Beyer H, Hibemann D, Mellinghoff H, Monnich K, Waldl H. Forecast of regional poweroutput of wind turbines[C]. European Wind Energy Conference, Nice,1999.
[61] Lange M, Focken U, Heinemann D. Previento-regional wind power prediction with riskcontrol[C]. World Wind Energy Conference, Berlin,2002.
[62] Wind energy forecasting: a collaboration of the national center for atmospheric research(NCAR) and Xcel Energy[R]. NREL/SR-5500-52233, Notional Renerwable EnergyLaboratory,2011.
[63] Cheng William Y.Y., Yubao Liu, Bill Mahoney, Warner, Thomas T, Badrinath Nagarajan.Feature-based verification for the high resolution NCAR-Xcel WRF-RTFDDA wind rampforecasts[C]. World Renew. Energy Forum,2012.
[64] Zack J.W., Brower M.C., Bailey B.H.. Validating of the forewind model in wind forecastingapplication[C]. EUWEC special topic conference wind power for the21stcentury, Kassel,Germany,2000.
[65] Marti Perez I. Wind forecasting activities[C]. The First IEA Joint Action Symposium on WindForecasting Techniques, Norrkoping,2000.
[66]陈德生,李培强,李欣然,邓威,肖园园,刘乾勇.基于小波变换的短期风速预测综合模型[J].电工电能新技术,2012,31(3):73-76.
[67]陈德生.基于小波变换的短期风速预测综合模型的研究[D].长沙:湖南大学硕士学位论文,2012.
[68] Bollerlevt. Generalized autoregressive conditional heteroskedasticity[J]. J. Econom.,1986,31(3):307-327.
[69]刘玄,冯彩.2005年以来我国股票市场波动特性研究——基于GARCH模型[J].经济论坛,2009,21(2):42-45.
[70]杨俊,艾欣,冯义.基于非参数GARCH模型的电价预测[J].电工技术学报,2008,23(10):135-142.
[71]周晖,方江晓,黄梅.风电功率GARCH预测模型的应用研究[J].电力系统保护与控制，2011,39(5):108:114.
[72]吴俊利,张步涵,王魁.基于Adaboost的BP神经网络改进算法在短期风速预测中的应用[J].电网技术,2012,36(9):221-225.
[73]武小梅,白银明,文福拴.基于RBF神经元网络的风电功率短期预测[J].电力系统保护与控制,2011,39(15):80-83.
[74]刘进宝,丁涛.基于径向基函数神经网络的风速预测[J].太阳能学报,2012,33(7):1131-1135.
[75]陈守煜.模糊模式识别交叉迭代模型与收敛性[J].大连理工大学学报,2001,41(3):264-267．
[76]茆美琴,周松林,苏建徽.基于脊波神经网络的短期风电功率预测[J].电力系统自动化,2011,35(7):70-74.
[77] Candes E J. Ridgelets: theory and applications[D]. Palo Alto, CA, USA: Stanford Univesity,1998.
[78]刘志刚,王晓茹,何正友.小波变换、神经网络和小波网络的函数逼近能力分析与比较[J].电力系统自动化,2002,26(20):39-44.
[79]张靠社,杨剑.基于Elm an神经网络的短期风电功率预测[J].电网与清洁能源,2012,28(12):87-91.
[80]张华,曾杰.基于支持向量机的风速预测模型研究[J].太阳能学报,2010,31(7):928-932.
[81]杜颖,卢继平,李青,邓颖玲.基于最小二乘支持向量机的风电场短期风速预测[J].电网技术,2008,32(15):62-66.
[82]曾杰，张华.基于最小二乘支持向量机的风速预测模型[J].电网技术,2009,33(18):144-147.
[83]张广明,袁宇浩,龚松建.基于改进最小二乘支持向量机方法的短期风速预测[J].上海交通大学学报,2011,45(8):1125-1129.
[84]孙斌，姚海涛.基于PSO优化LSSVM的短期风速预测[J].电力系统保护与控制,2012,40(5):85-89.
[85]栗然,陈倩,徐宏锐.考虑相关因素的最小二乘支持向量机风速预测方法[J].电力系统保护与控制,2010,38(21):146-151.
[86][a48]李俊芳,张步涵,谢光龙,李妍,毛承雄.基于灰色模型的风速-风电功率预测研究[J].电力系统保护与控制,2010,38(19):151-159.
[87] Fouly T, Saadany E, Salama M. Grey predictor for wind energy conversion systems outputpower prediction[J]. IEEE Trans. Power Syst.,2006,21(3):1450-1452.
[88] El-Fouly T H M, El-Saadany E F, Salama M M A. Improved grey predictor rolling models forwind power prediction[J]. IEEE Proceedings on Gener., Transm.&Distrib.,2007,1(6):928-937.
[89] M.C.Alexiadis, P.S.Dokopoulos, H.S.Sahsamanoglou. Wind speed and power forecastingbased on spatial correlation models[J]. IEEE Trans. Energy Convers.,1999,14(3):836-842.
[90]陈妮亚,钱政,孟晓风,孟凯峰.基于空间相关法的风电场风速多步预测模型[J].电工技术学报,2013,28(5):15-21.
[91] A. Sfetsos. A comparison of various forecasting techniques applied to mean hourly wind speedtime series[J]. Renew. Energy,2000,21:23-25.
[92] Alexiadis M,Dokopoulos P, Sahsamanoglou H, Short term forecasting of wind speed andrelated electrical power[J]. Solar Energy,1998,63(1):61-68.
[93]曾鸣,沈又幸,吕俊昌.基于空间平滑法的风电场短期出力预测模型[J].现代电力,2008,25(3):81-84.
[94]孙春顺,王耀南,李欣然.小时风速的向量自回归模型及应用[J].中国电机工程学报,2008,28(14):112-117.
[95]赵辉,李斌,李彪,岳有军.基于小波变换的ARMA-LS SVM短期风速预测[J].中国电力,2012,45(4):78-81.
[96]胡建明.基于聚合经验模态分解的时间序列模型的研究及其应用[D].兰州:兰州大学硕士学位论文,2012.
[97]蔡凯,谭伦农,李春林,陶雪峰.时间序列与神经网络法相结合的短期风速预测[J].电网技术,2008,32(8):82-85.
[98]张国强,张伯明.基于组合预测的风电场风速及风电机功率预测[J].电力系统自动化,2009,33(18):92-95.
[99]范高锋,王伟胜,刘纯,戴慧珠.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34):118-123.
[100]王松岩,李碧君,于继来,徐泰山.风速与风电功率预测误差概率分布的时变特性分析[J].中国电机工程学报,2012,36(0):1-8.
[101]朱德恒,严璋,谈克雄.电气设备状态监测与故障诊断技术[M].北京:中国电力出版社.2009.
[102]何剑,程林,孙元章,王鹏.条件相依的输变电设备短期可靠性模型[J].中国电机工程学报,2009,29(7):39-46.
[103]国家电监会.风电安全监管报告(2011)[EB/OL]． http://ishare.iask.sina.com.cn/f/23485464.html.
[104] Johan Ribrant, Lina Margareta Bertling. Survey of failures in wind power systems with focuson Swedish wind power plants during1997–2005[J]. IEEE Trans. Energy Convers.,2001,22(1):167-173.
[105] Kevin Alewine, William Chen. A review of electrical winding failures in wind turbinegenerators[J]. IEEE Electr. Insul. Mag.,2012,28(4):8-13.
[106]高社生，张玲霞.可靠性理论与工程应用[M].北京:国防工业出版社，2002.
[107]金星,洪延姬.系统可靠性与可用性分析方法[M].北京:国防工业出版社,2007.
[108] Riehard E. Barlow. Mathematical theory of reliability: A historical perspective [J]. IEEE Trans.Reliab.,1984,33(1):16-20.
[109] W.S. Lee, D.L. Grosh, F.A. Tillman, C.H. Lie. Fault tree analysis, methods, and applications-Areview [J]. IEEE Trans. Reliab.,1985,34(3):194-203.
[110]林闯.随机Perti网和系统性能评价(第一版)[M].北京:清华大学出版社,2000.
[111]贾西胜.以可靠性为中心的维修[J].工程机械与维修,2002,(11):116-117.
[112] R.A. Sahner, K.S. Trivedi, A. Puliafito. Performance and reliability analysis of computersystems [M]. Norwell, Massachusetts, USA: Kluwer Academic Publishers,1996.
[113]霍利民,朱永利,张立国,黄丽华,于尧.用于电力系统可靠性评估的贝叶斯网络时序模拟推理算法[J].电工技术学报,2008,23(6):89-95.
[114]周忠宝,董豆豆,周经伦.贝叶斯网络在可靠性分析中的应用[J].系统工程理论与实践,2006,26(6):95-100.
[115]朱萍,杨昆.设备可靠性的贝叶斯评估[J].现代电力,2004,21(6):11-14.
[116] H. Arabian-Hoseynabadi, H. Oraee, P.J. Tavner. Failure modes and effects analysis (FMEA) forwind turbines[J]. Int. J. Electr. Power Energy Syst.,2010,32:817-824.
[117] Das M.K., Panja S.C., Chowdhury S., Chowdhury. S.P., Elombo A.I. Expert-Based FMEA ofWind Turbine System[C]. IEEE International Conference on Industrial Engineering andEngineering Management, Singapore, Singapore,2011:1582-1585.
[118] Kahrobaee Salman, Asgarpoor Sohrab. Risk-based failure mode and effect analysis for windturbines (RB-FMEA)[C]. NAPS2011-43rd North American Power Symposium, Boston, MA,United states,2011.
[119]何成兵,顾煜炯,邢诚.基于FMEA法的风力发电机组故障模式分析[J].可再生能源,2011,29(3):120-126.
[120]李大字,冯园园,刘展,楚纪正,靳其兵.风力发电机组可靠性建模与维修策略优化[J].电网技术,2011,35(9):122-127.
[121] H. Arabian-Hoseynabadi, H. Oraee, P.J. Tavner. Wind turbine productivity consideringelectrical subassembly reliability[J]. Renew. Energy,2010,35:190-197.
[122]舒进,郝治国,张保会,薄志谦.风电场的集群功率优化控制[J].中国电机工程学报,2011,31(34):10-19.
[123] Anca D H，Poul S，Florin I，et al．Centralized power control of wind farm with doubly fedinduction generators[J]. Renew. Energy,2006,31(7):935-951．
[124]黄崇鑫,张凯锋,戴先中.考虑DFIG机组容量限制的风电场功率分配方法[J].电力系统保护与控制,2010,38(21):202-207．
[125]张利,王成福,牛远方.风电场输出有功功率的协调分配策略[J].电力自动化设备,2012,32(8):101-105.
[126]邹见效,袁炀,黄其平,郑刚,曾斌,彭超.风电场有功功率控制降功率优化算法[J].电子科技大学学报,2011,40(6):882-886.
[127]汤奕,王琦,陈宁,朱凌志.采用功率预测信息的风电场有功优化控制方法[J].中国电机工程学报,2012,32(34):1-8.
[128]惠晶,顾鑫.大型风电场的集中功率控制策略研究[J].华东电力,2008,36(6):57-60.
[129]乔颖,鲁宗相.考虑电网约束的风电场自动有功控制[J].电力系统自动化,2009,33(22):88-93.
[130]王世谦.基于双馈风电机组的风电场有功功率[D].北京:华北电力大学硕士学位论文,2012.
[131]李雪明,行舟,陈振寰,陈永华,王福军,罗剑波.大型集群风电有功智能控制系统设计[J].电力系统自动化,2010,34(17):88-93.
[132]尹远.双馈风电机组与电池储能设备的有功功率协调控制研究[J].重庆：重庆大学硕士论文,2012.
[133] B.L.S. Prakasa Rao. Nonparametric function estimation [M].Academic Press: London,1983.
[134] M.P. Wand, M.C. Jones Kernel Smoothing [M]. Chapman and Hall: London,1995.
[135]金星.工程系统可靠性数值分析方法[M].北京:国防工业出版社,2002.
[136] Riehard E. Barlow. Mathematical theory of reliability: A historical perspective [J]. IEEETransactions on Reliability,1984,33(1):16-20.
[137] R.A. Sahner, K.S. Trivedi, A. Puliafito. Performance and reliability analysis of computersystems [M]. Norwell, Massachusetts, USA: Kluwer Academic Publishers,1996.
[138]徐钟济.蒙特卡罗方法[M].上海:上海科学技术出版社,1985.
[139]朱德恒,严璋,谈克雄.电气设备状态监测与故障诊断技术[M].北京:中国电力出版社.2009.
[140] Lin Cheng, Jin Lin, Yuan-Zhang Sun, Chanan Singh, Wen-Zhong Gao, Xing-Mei Qin. A modelfor assessing the power variation of a wind farm considering the outages of wind turibines [J].IEEE Trans. Sustainable Energy,2012,3(3):432-444.
[141] Jian Li, Zhiman He, Grzybowski S., Electrical aging lifetime model of oil-impregnated paperunder pulsating DC voltage influenced by temperature[J]. IEEE Trans. Dielectr. Electr. Insul.,2013,20(6):1992-1997.
[142]王松岩，于继来.风速与风电功率的联合条件概率预测方法[J].中国电机工程学报,2011,31(7):7-15.
[143] Andrew Kusiak, Anoop Verma. Wind turbine condition monitoring based on SCADA datausing normal behavior models. Part1: System description[J]. Appl. Soft Comput. J.,2014,14:447-460.
[144] M. Rosenblatt. Remarks on some nonparametric estimates of a density function [J]. Annals ofMathematical Statics,1956,27(3),832-837.
[145] Kazuki Ogimi, Shota Kamiyama, Michael Palmer, Atsushi Yona, Tomonobu Senju, ToshihisaFunabashi. Optimal Operation Planning of Wind Farm Installed BESS Using Wind PowerForecast Data of Wind Turbine Generators Considering Forecast Error[J]. Int. J. Emerg. Electr.Power Syst.,2013,14(3):207-218.
[146]张国强,张伯明.考虑风电接入后二次备用需求的优化潮流算法[J].电力系统自动化,2009,33(8):25-28.
[147]赵明欣,鲁宗相,吴林林,余邵峰.基于风险评估的输变电设备维修技术[J].电力系统自动化,2009,33(19):30-35.
[148] McCalley J.D., Vittal V., Wan. Voltage risk assessment [C]. Pro. IEEE Power EngineeringSociety Summer Meeting. Edmonton, Canada,1999:179-184.
[149] The New IEEE Standard Dictionary of Electrical and Electronic Terms [S].5th ed.1993.
[2]国家发改委能源所和IEA.中国风电发展路线图2050[R].2011.
[3]蒋泽甫.风电转换系统可靠性评估及其薄弱环节辨识[D].重庆:重庆大学博士学位论文.2012.
[4]杨校生.风力发电技术与风电场工程[M].北京:化学工业出版社,2011.
[5] Tavner P.J., Spinato F., van Bussel G.J.W., Koutoulakos E.. Reliability of different windturbine concepts with relevance to offshore application[C]. European Wind Energy Conference,Brussels, Belgium,2008.
[6] D. McMillan, G.W. Ault. Condition monitoring benefit for onshore wind turbines: sensitivityto operational parameters[J]. IET Renew. Power Gener.,2008,2(1):60–72.
[7] The European Wind Energy Association. Wind power economics[R].2010.
[8] Tavner P.J., J. Xiang, F. Spinato. Reliability analysis for wind turbines[J]. Wind Energy,2007,10:1-8.
[9] F. Spinato1, P.J. Tavner1, G.J.W. van Bussel, E. Koutoulakos. Reliability of wind turbinesubassemblies[J]. IET Renew. Power Gener.,2009,3(4):387-401.
[10] Felanalys, Database of failures for Swedish wind power turbines1997–2005[DB],2007.
[11] Elforsk N.E. Carlstedt, C. Szadkowski, C. Karlstr¨om Elforskrapporter. Performance of windpower plants, annual report,1997–2004[EB/OL]．http://www.elforsk.se
[12] H. Holttinen, T. Lakso, and M. Marjaniemi. Performance of wind power plants, annual report,2000–2005[EB/OL]. http://www.vtt.fi
[13] P J Tavner, R Gindele, S Faulstich. Study of effects of weather&location on wind turbinefailure rates[C]. European Wind Energy Conference, Warsaw, Poland,2010.
[14] Kaigui Xie, Zefu Jiang, Wenyuan Li. Effect of wind speed on wind turbine power converterreliability[J]. IEEE Trans. Energy Convers.,2012,27(1):96-104.
[15]蒋程,张建华,刘先正,于坤山.计及运行工况的风电机组停运模型[J],电力系统保护与控制,2013,41(24):112-116.
[16]高俊峰.中国风电发展报告2012[M].北京:中国环境科学出版社.2012.
[17]中华人民共和国国家质量监督检验检疫总局,中华人民共和国国家标准GB/T19963-2011,风电场接入电力系统技术规定[S].
[18]国家电网公司,风电场接入电网技术规定实施细则[S].
[19] Ghoshal A, Sundaresan MJ, Schulz MJ, Pai PF. Structural health monitoring techniques forwind turbines blades[J]. J. Wind Eng. Ind. Aerodyn.,2000,85:309-324.
[20] Cozens NJ, Watson SJ. State of the art condition monitoring techniques suitable for windturbines and wind farm applications[R]. Report for CONMOW project;2003.
[21] Caselitz P, Giebhardt J. Fault prediction techniques for offshore wind farm maintenance andrepair strategies[C]. Annual Conference of the IEEE Industrial Electronics Society, Vienna,Austria,2003.
[22] Caselitz P, Giebhardt J, Mevenkamp M. On-line fault detection and prediction in wind energyconverters[C]. Annual Conference of the IEEE Industrial Electronics Society, Thessaloniki,Greece,1994:623-627.
[23] Futter DN. Vibration monitoring of industrial gearboxes using time domain averaging[C].IMechE2nd International conference on gearbox noise, vibration and diagnostics,1995.
[24] Hong J-C, Kim YY, Lee HC, Lee YW. Damage detection using the Lipschitz exponentestimated by the wavelet transform: application to vibration modes of a Beam[J]. Int. J. SolidsStruct.,2002,39:1803-1816.
[25] Sun Q, Tang Y, Yang W, Ji Y. Feature extraction with discrete wavelet transform for drill wearmonitoring[J]. J. Vib. Control,2005,11:1375-1396.
[26] Nikolaou NG, Antoniadis IA. Rolling element bearing fault diagnosis using wavelet packets[J].NDT E Int.,2002,35(3):197-205.
[27] Ziola S．Digital signal processing of modal emission signals[J]. J. Acoust. Emiss.,1996,14:12-18．
[28] Tandon N, Nakra BC. Defect detection in rolling element bearings by acoustic emissionmethod[J]. J. Acoust. Emiss.,1990,9(1):25-28.
[29] Tan CC. Application of acoustic emission to the detection of bearing failures[C]. TribologyConference, Brisbane,1990:110-114.
[30] Blanch MJ, Dutton AG. Acoustic emission monitoring of field Tests of an operating windturbine[J]. Key Eng. Mat.,2003,245:475-482.
[31] Jungert A. Damage detection in wind turbine blades using two different acoustic techniques[J].The e-Journal of Nondestructive Testing,2008,25.
[32]陈长征,赵新光,周勃,谷泉.风电机组叶片裂纹故障特征提取方法[J].中国电机工程学报,2013,33(2):112-118.
[33] Juengert A, Grosse CU. Inspection techniques for wind turbine blades using ultrasound andsound waves[C]. Non-destructive testing in Civil Engineering, Nantes, France,2009.
[34] Jasiuniene E, Raisutis R, Sliteris R, Voleisis A, Vladisauskas A, Mitchard D. NDT of windturbine blades using adapted ultrasonic and radiographic techniques[J].Insight-Non-Destructive Testing and Condition Monitoring,2009,51(9):477-483.
[35] Schoen RR, Lin BK, Habetler TG, Schlag JH, Farag S. An unsupervised, on-line system forinduction motor fault detection using stator current monitoring[J]. IEEE Trans. Ind. Appl.,1995,31(6):1280-1286.
[36] Wenxian Yang, Peter J. Tavner, Christopher J. Crabtree, Michael Wilkinson. Cost-EffectiveCondition Monitoring for Wind Turbines[J]. IEEE Trans. Ind. Electron.,2010,57(1):263-271.
[37]龙泉,刘永前,杨勇平.状态监测与故障诊断在风电机组上的应用[J].现代电力,2008,25(6):55-59.
[38] Schroeder K, Ecke W, Apitz J, Lembke E, Lenschow G. A fibre Bragg grating sensor systemmonitors operational load in a wind turbine rotor blade[J]. Meas. Sci. Technol.,2006,17(5):1167-1172.
[39] Combet L, Gelman L. An automated methodology for performing time synchronous averagingof a gearbox signal without speed sensor[J]. Mech. Syst. Signal Process,2007,21(6):2590-2606.
[40] Caselitz P, Giebhargt J, Kruger T, Mevenkamp M. Development of a Fault Detection Systemfor Wind Energy Converters[C]. Proceedings of the EUWEC’96, Gotegorg,1996:1004-1007.
[41] Caselitz P, Giebhargt J, Rotor condition monitoring for improved operational safety of offshorewind energy converters[J]. J. Sol. Energy Eng. Trans. ASME,127:253-261.
[42] Staszewski WJ, Tomlinson GR. Application of the wavelet transform to fault detection in aSpur gear[J]. Mech. Syst. Signal Process,1994,8:289-307.
[43] Wang WJ, McFadden PD. Application of wavelets to gearbox vibration signals for faultdetection[J]. J. Sound Vib.,1996,192:927-939.
[44] Luo GY, Osypiw D, Irle M. On-Line vibration analysis with fast continuous wavelet algorithmfor condition monitoring of bearing[J]. J. Vib. Control,2003,9:931-947.
[45] Rubini R, Meneghetti U. Application of the envelope and wavelet transform analyses for thediagnosis of incipient faults in ball bearings[J]. Mech. Syst. Signal Process,2001,15:287-302.
[46] Dawei Xiang, Li Ran, Peter Tavner, Angus Bryant, Shaoyong Yang, Philip Mawby. MonitoringSolder Fatigue in a Power Module Using Case-Above-Ambient Temperature Rise[J]. IEEETrans. Ind. Appl.,2011,47(6):2578-2591.
[47]郭鹏,徐明,白楠,马登昌.基于SCADA运行数据的风电机组塔架振动建模与监测[J].中国电机工程学报,2013,33(5):128-136.
[48]郭鹏, David Infield,杨锡运.风电机组齿轮箱温度趋势状态监测及分析方法[J].中国电机工程学报,2013,31(32):129-136.
[49] Meik Schlechtingen, Ilmar Ferreira Santos. Comparative analysis of neural network andregression based condition monitoring approaches for wind turbine fault detection[J]. Mech.Syst. Signal Process,2011,25(5):1849-1875.
[50] Andrew Kusiak, Anoop Verma. Wind turbine condition monitoring based on SCADA datausing normal behavior models. Part1: System description[J]. Appl. Soft Comput. J.,2014,14:447-460.
[51] Andrew Kusiak, Anoop Verma. Analyzing bearing faults in wind turbines: A data-miningapproach[J]. Renew. Energy,2012,48:110-116.
[52]李辉,胡姚刚,唐显虎,刘志详.并网风电机组在线运行状态评估方法[J].中国电机工程学报,2010,30(33):103-109.
[53]李辉,胡姚刚,杨超,李学伟,唐显虎.并网风电机组运行状态的物元评估方法[J].电力系统自动化,2011,35(6):81-85.
[54]董玉亮,李亚琼,曹海斌,何成兵,顾煜炯.基于运行工况辨识的风电机组健康状态实时评价方法[J].2013,33(11):88-95.
[55] Wegley H, Formica W. Test applications of a semi-objective approach to wind forecasting forwind forecasting for wind energy applictions[R]. PNL-4403, Pacific Northwest Laboratory,1983.
[56] Troen I, Landberg L. Short term predietion of local wind conditions[C]. EuroPean CommunityWind Energy Conference, Madrid, Spain,1990:76-78.
[57] Landberg L. A mathematical look at a Physical Power Predietion model[J]. Wind Energy,1998,l:23-28.
[58] Watson R, Landberg L, Costello R, McCoy D, O’Donnell P. Evaluation of the Prediktor windpower forecasting system in Ireland[C]. European wind energy Conference, Copenhagen,2001.
[59] G. Giebel, L. Landberg, T.S. Nielsen. The Zephyr-Project: the next generation predictionsystem[C]. Poster P_GWP145on the Global Wind power conference and Exhibition,2002.
[60] Beyer H, Hibemann D, Mellinghoff H, Monnich K, Waldl H. Forecast of regional poweroutput of wind turbines[C]. European Wind Energy Conference, Nice,1999.
[61] Lange M, Focken U, Heinemann D. Previento-regional wind power prediction with riskcontrol[C]. World Wind Energy Conference, Berlin,2002.
[62] Wind energy forecasting: a collaboration of the national center for atmospheric research(NCAR) and Xcel Energy[R]. NREL/SR-5500-52233, Notional Renerwable EnergyLaboratory,2011.
[63] Cheng William Y.Y., Yubao Liu, Bill Mahoney, Warner, Thomas T, Badrinath Nagarajan.Feature-based verification for the high resolution NCAR-Xcel WRF-RTFDDA wind rampforecasts[C]. World Renew. Energy Forum,2012.
[64] Zack J.W., Brower M.C., Bailey B.H.. Validating of the forewind model in wind forecastingapplication[C]. EUWEC special topic conference wind power for the21stcentury, Kassel,Germany,2000.
[65] Marti Perez I. Wind forecasting activities[C]. The First IEA Joint Action Symposium on WindForecasting Techniques, Norrkoping,2000.
[66]陈德生,李培强,李欣然,邓威,肖园园,刘乾勇.基于小波变换的短期风速预测综合模型[J].电工电能新技术,2012,31(3):73-76.
[67]陈德生.基于小波变换的短期风速预测综合模型的研究[D].长沙:湖南大学硕士学位论文,2012.
[68] Bollerlevt. Generalized autoregressive conditional heteroskedasticity[J]. J. Econom.,1986,31(3):307-327.
[69]刘玄,冯彩.2005年以来我国股票市场波动特性研究——基于GARCH模型[J].经济论坛,2009,21(2):42-45.
[70]杨俊,艾欣,冯义.基于非参数GARCH模型的电价预测[J].电工技术学报,2008,23(10):135-142.
[71]周晖,方江晓,黄梅.风电功率GARCH预测模型的应用研究[J].电力系统保护与控制，2011,39(5):108:114.
[72]吴俊利,张步涵,王魁.基于Adaboost的BP神经网络改进算法在短期风速预测中的应用[J].电网技术,2012,36(9):221-225.
[73]武小梅,白银明,文福拴.基于RBF神经元网络的风电功率短期预测[J].电力系统保护与控制,2011,39(15):80-83.
[74]刘进宝,丁涛.基于径向基函数神经网络的风速预测[J].太阳能学报,2012,33(7):1131-1135.
[75]陈守煜.模糊模式识别交叉迭代模型与收敛性[J].大连理工大学学报,2001,41(3):264-267．
[76]茆美琴,周松林,苏建徽.基于脊波神经网络的短期风电功率预测[J].电力系统自动化,2011,35(7):70-74.
[77] Candes E J. Ridgelets: theory and applications[D]. Palo Alto, CA, USA: Stanford Univesity,1998.
[78]刘志刚,王晓茹,何正友.小波变换、神经网络和小波网络的函数逼近能力分析与比较[J].电力系统自动化,2002,26(20):39-44.
[79]张靠社,杨剑.基于Elm an神经网络的短期风电功率预测[J].电网与清洁能源,2012,28(12):87-91.
[80]张华,曾杰.基于支持向量机的风速预测模型研究[J].太阳能学报,2010,31(7):928-932.
[81]杜颖,卢继平,李青,邓颖玲.基于最小二乘支持向量机的风电场短期风速预测[J].电网技术,2008,32(15):62-66.
[82]曾杰，张华.基于最小二乘支持向量机的风速预测模型[J].电网技术,2009,33(18):144-147.
[83]张广明,袁宇浩,龚松建.基于改进最小二乘支持向量机方法的短期风速预测[J].上海交通大学学报,2011,45(8):1125-1129.
[84]孙斌，姚海涛.基于PSO优化LSSVM的短期风速预测[J].电力系统保护与控制,2012,40(5):85-89.
[85]栗然,陈倩,徐宏锐.考虑相关因素的最小二乘支持向量机风速预测方法[J].电力系统保护与控制,2010,38(21):146-151.
[86][a48]李俊芳,张步涵,谢光龙,李妍,毛承雄.基于灰色模型的风速-风电功率预测研究[J].电力系统保护与控制,2010,38(19):151-159.
[87] Fouly T, Saadany E, Salama M. Grey predictor for wind energy conversion systems outputpower prediction[J]. IEEE Trans. Power Syst.,2006,21(3):1450-1452.
[88] El-Fouly T H M, El-Saadany E F, Salama M M A. Improved grey predictor rolling models forwind power prediction[J]. IEEE Proceedings on Gener., Transm.&Distrib.,2007,1(6):928-937.
[89] M.C.Alexiadis, P.S.Dokopoulos, H.S.Sahsamanoglou. Wind speed and power forecastingbased on spatial correlation models[J]. IEEE Trans. Energy Convers.,1999,14(3):836-842.
[90]陈妮亚,钱政,孟晓风,孟凯峰.基于空间相关法的风电场风速多步预测模型[J].电工技术学报,2013,28(5):15-21.
[91] A. Sfetsos. A comparison of various forecasting techniques applied to mean hourly wind speedtime series[J]. Renew. Energy,2000,21:23-25.
[92] Alexiadis M,Dokopoulos P, Sahsamanoglou H, Short term forecasting of wind speed andrelated electrical power[J]. Solar Energy,1998,63(1):61-68.
[93]曾鸣,沈又幸,吕俊昌.基于空间平滑法的风电场短期出力预测模型[J].现代电力,2008,25(3):81-84.
[94]孙春顺,王耀南,李欣然.小时风速的向量自回归模型及应用[J].中国电机工程学报,2008,28(14):112-117.
[95]赵辉,李斌,李彪,岳有军.基于小波变换的ARMA-LS SVM短期风速预测[J].中国电力,2012,45(4):78-81.
[96]胡建明.基于聚合经验模态分解的时间序列模型的研究及其应用[D].兰州:兰州大学硕士学位论文,2012.
[97]蔡凯,谭伦农,李春林,陶雪峰.时间序列与神经网络法相结合的短期风速预测[J].电网技术,2008,32(8):82-85.
[98]张国强,张伯明.基于组合预测的风电场风速及风电机功率预测[J].电力系统自动化,2009,33(18):92-95.
[99]范高锋,王伟胜,刘纯,戴慧珠.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34):118-123.
[100]王松岩,李碧君,于继来,徐泰山.风速与风电功率预测误差概率分布的时变特性分析[J].中国电机工程学报,2012,36(0):1-8.
[101]朱德恒,严璋,谈克雄.电气设备状态监测与故障诊断技术[M].北京:中国电力出版社.2009.
[102]何剑,程林,孙元章,王鹏.条件相依的输变电设备短期可靠性模型[J].中国电机工程学报,2009,29(7):39-46.
[103]国家电监会.风电安全监管报告(2011)[EB/OL]． http://ishare.iask.sina.com.cn/f/23485464.html.
[104] Johan Ribrant, Lina Margareta Bertling. Survey of failures in wind power systems with focuson Swedish wind power plants during1997–2005[J]. IEEE Trans. Energy Convers.,2001,22(1):167-173.
[105] Kevin Alewine, William Chen. A review of electrical winding failures in wind turbinegenerators[J]. IEEE Electr. Insul. Mag.,2012,28(4):8-13.
[106]高社生，张玲霞.可靠性理论与工程应用[M].北京:国防工业出版社，2002.
[107]金星,洪延姬.系统可靠性与可用性分析方法[M].北京:国防工业出版社,2007.
[108] Riehard E. Barlow. Mathematical theory of reliability: A historical perspective [J]. IEEE Trans.Reliab.,1984,33(1):16-20.
[109] W.S. Lee, D.L. Grosh, F.A. Tillman, C.H. Lie. Fault tree analysis, methods, and applications-Areview [J]. IEEE Trans. Reliab.,1985,34(3):194-203.
[110]林闯.随机Perti网和系统性能评价(第一版)[M].北京:清华大学出版社,2000.
[111]贾西胜.以可靠性为中心的维修[J].工程机械与维修,2002,(11):116-117.
[112] R.A. Sahner, K.S. Trivedi, A. Puliafito. Performance and reliability analysis of computersystems [M]. Norwell, Massachusetts, USA: Kluwer Academic Publishers,1996.
[113]霍利民,朱永利,张立国,黄丽华,于尧.用于电力系统可靠性评估的贝叶斯网络时序模拟推理算法[J].电工技术学报,2008,23(6):89-95.
[114]周忠宝,董豆豆,周经伦.贝叶斯网络在可靠性分析中的应用[J].系统工程理论与实践,2006,26(6):95-100.
[115]朱萍,杨昆.设备可靠性的贝叶斯评估[J].现代电力,2004,21(6):11-14.
[116] H. Arabian-Hoseynabadi, H. Oraee, P.J. Tavner. Failure modes and effects analysis (FMEA) forwind turbines[J]. Int. J. Electr. Power Energy Syst.,2010,32:817-824.
[117] Das M.K., Panja S.C., Chowdhury S., Chowdhury. S.P., Elombo A.I. Expert-Based FMEA ofWind Turbine System[C]. IEEE International Conference on Industrial Engineering andEngineering Management, Singapore, Singapore,2011:1582-1585.
[118] Kahrobaee Salman, Asgarpoor Sohrab. Risk-based failure mode and effect analysis for windturbines (RB-FMEA)[C]. NAPS2011-43rd North American Power Symposium, Boston, MA,United states,2011.
[119]何成兵,顾煜炯,邢诚.基于FMEA法的风力发电机组故障模式分析[J].可再生能源,2011,29(3):120-126.
[120]李大字,冯园园,刘展,楚纪正,靳其兵.风力发电机组可靠性建模与维修策略优化[J].电网技术,2011,35(9):122-127.
[121] H. Arabian-Hoseynabadi, H. Oraee, P.J. Tavner. Wind turbine productivity consideringelectrical subassembly reliability[J]. Renew. Energy,2010,35:190-197.
[122]舒进,郝治国,张保会,薄志谦.风电场的集群功率优化控制[J].中国电机工程学报,2011,31(34):10-19.
[123] Anca D H，Poul S，Florin I，et al．Centralized power control of wind farm with doubly fedinduction generators[J]. Renew. Energy,2006,31(7):935-951．
[124]黄崇鑫,张凯锋,戴先中.考虑DFIG机组容量限制的风电场功率分配方法[J].电力系统保护与控制,2010,38(21):202-207．
[125]张利,王成福,牛远方.风电场输出有功功率的协调分配策略[J].电力自动化设备,2012,32(8):101-105.
[126]邹见效,袁炀,黄其平,郑刚,曾斌,彭超.风电场有功功率控制降功率优化算法[J].电子科技大学学报,2011,40(6):882-886.
[127]汤奕,王琦,陈宁,朱凌志.采用功率预测信息的风电场有功优化控制方法[J].中国电机工程学报,2012,32(34):1-8.
[128]惠晶,顾鑫.大型风电场的集中功率控制策略研究[J].华东电力,2008,36(6):57-60.
[129]乔颖,鲁宗相.考虑电网约束的风电场自动有功控制[J].电力系统自动化,2009,33(22):88-93.
[130]王世谦.基于双馈风电机组的风电场有功功率[D].北京:华北电力大学硕士学位论文,2012.
[131]李雪明,行舟,陈振寰,陈永华,王福军,罗剑波.大型集群风电有功智能控制系统设计[J].电力系统自动化,2010,34(17):88-93.
[132]尹远.双馈风电机组与电池储能设备的有功功率协调控制研究[J].重庆：重庆大学硕士论文,2012.
[133] B.L.S. Prakasa Rao. Nonparametric function estimation [M].Academic Press: London,1983.
[134] M.P. Wand, M.C. Jones Kernel Smoothing [M]. Chapman and Hall: London,1995.
[135]金星.工程系统可靠性数值分析方法[M].北京:国防工业出版社,2002.
[136] Riehard E. Barlow. Mathematical theory of reliability: A historical perspective [J]. IEEETransactions on Reliability,1984,33(1):16-20.
[137] R.A. Sahner, K.S. Trivedi, A. Puliafito. Performance and reliability analysis of computersystems [M]. Norwell, Massachusetts, USA: Kluwer Academic Publishers,1996.
[138]徐钟济.蒙特卡罗方法[M].上海:上海科学技术出版社,1985.
[139]朱德恒,严璋,谈克雄.电气设备状态监测与故障诊断技术[M].北京:中国电力出版社.2009.
[140] Lin Cheng, Jin Lin, Yuan-Zhang Sun, Chanan Singh, Wen-Zhong Gao, Xing-Mei Qin. A modelfor assessing the power variation of a wind farm considering the outages of wind turibines [J].IEEE Trans. Sustainable Energy,2012,3(3):432-444.
[141] Jian Li, Zhiman He, Grzybowski S., Electrical aging lifetime model of oil-impregnated paperunder pulsating DC voltage influenced by temperature[J]. IEEE Trans. Dielectr. Electr. Insul.,2013,20(6):1992-1997.
[142]王松岩，于继来.风速与风电功率的联合条件概率预测方法[J].中国电机工程学报,2011,31(7):7-15.
[143] Andrew Kusiak, Anoop Verma. Wind turbine condition monitoring based on SCADA datausing normal behavior models. Part1: System description[J]. Appl. Soft Comput. J.,2014,14:447-460.
[144] M. Rosenblatt. Remarks on some nonparametric estimates of a density function [J]. Annals ofMathematical Statics,1956,27(3),832-837.
[145] Kazuki Ogimi, Shota Kamiyama, Michael Palmer, Atsushi Yona, Tomonobu Senju, ToshihisaFunabashi. Optimal Operation Planning of Wind Farm Installed BESS Using Wind PowerForecast Data of Wind Turbine Generators Considering Forecast Error[J]. Int. J. Emerg. Electr.Power Syst.,2013,14(3):207-218.
[146]张国强,张伯明.考虑风电接入后二次备用需求的优化潮流算法[J].电力系统自动化,2009,33(8):25-28.
[147]赵明欣,鲁宗相,吴林林,余邵峰.基于风险评估的输变电设备维修技术[J].电力系统自动化,2009,33(19):30-35.
[148] McCalley J.D., Vittal V., Wan. Voltage risk assessment [C]. Pro. IEEE Power EngineeringSociety Summer Meeting. Edmonton, Canada,1999:179-184.
[149] The New IEEE Standard Dictionary of Electrical and Electronic Terms [S].5th ed.1993.