基于线性插值模型的大型风电机组服役性能在线评估方法
|
摘要
针对服役风电机组的在线评估问题,提出一种基于线性插值模型的风电机组服役性能指标计算及评估方法。该方法包括线性插值模型的建立和服役指标的计算及评估。线性插值模型的建立将采用理论分析和统计分析相结合的数据预处理方法,基于预处理后的数据采用比恩法均匀划分区间,并在区间中统计各定义指标的功率,然后构造指标关于功率的线性插值模型。服役指标的计算及评估是通过风电机组的原始数据,以时间滑动的方式,对每次滑动的数据利用比恩法划分区间并应用线性插值模型计算性能指标,同时计算区间分配的权值,由性能指标和权值整合成机组每次滑动的服役性能指标,获得机组的服役性能指标趋势图。利用SCADA数据进行验证,证明方法的有效性,完成机组服役性能的评估工作。
Aiming at the on-line evaluated problem of commissioned wind turbines,a method for calculating and evaluating the commissioned performance of wind turbines based on linear interpolation model is proposed. The method includes the establishment of a linear interpolation model and the calculation and evaluation of commissioned indicators. The establishment of the linear interpolation model adopts a data preprocessing method combining theoretical analysis and statistical analysis. The method of Bin is used to evenly divide the interval based on the pre-processed data,and the power of each defined index is counted in the interval,and then the linear interpolation model of power for the indicator is constructed. The calculation and evaluation of the commissioned indicators is performed through the original data of the wind turbine. In the time sliding manner,the data of each sliding is divided by the method of Bin and the linear interpolation model is applied to calculate the performance index; at the same time, the weight of the interval allocation is calculated. The commissioned performance indicators of the wind turbine is composed of index and weights after each sliding to obtain the trend chart of the commissioned performance index of the wind turbine. The SCADA data is used for effectiveness verification,and the evaluation of the commissioned performance of the wind turbine is completed.
Aiming at the on-line evaluated problem of commissioned wind turbines,a method for calculating and evaluating the commissioned performance of wind turbines based on linear interpolation model is proposed. The method includes the establishment of a linear interpolation model and the calculation and evaluation of commissioned indicators. The establishment of the linear interpolation model adopts a data preprocessing method combining theoretical analysis and statistical analysis. The method of Bin is used to evenly divide the interval based on the pre-processed data,and the power of each defined index is counted in the interval,and then the linear interpolation model of power for the indicator is constructed. The calculation and evaluation of the commissioned indicators is performed through the original data of the wind turbine. In the time sliding manner,the data of each sliding is divided by the method of Bin and the linear interpolation model is applied to calculate the performance index; at the same time, the weight of the interval allocation is calculated. The commissioned performance indicators of the wind turbine is composed of index and weights after each sliding to obtain the trend chart of the commissioned performance index of the wind turbine. The SCADA data is used for effectiveness verification,and the evaluation of the commissioned performance of the wind turbine is completed.
引文
[1] LEE J,WU F,ZHAO W,et al. Prognostics and health management design for rotary machinery systemsReviews, methodology and applications[J].Mechanical Systems&Signal Processing,2014,42(1-2):314-334.
[2] JIA X, JIN C, BUZZA M, et al. Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves[J].Renewable Energy,2016,99:1191-1201.
[3] ULUYOL O,PARTHASARATHY G,FOSLIEN W,et al. Power curve analytic for wind turbine performance monitoring and prognostics[C]//Annual conference of the prognostics and health management society.2011:1-8.
[4] ALEXANDROS B, NIKOS P, BABIS M, et al.Enabling condition-based maintenance decisions with proactive event-driven computing[J]. Computers in Industry,2018,100:173-183.
[5] JIANZHONG S,CHAOYI L I,CUI L,et al. A data-driven health indicator extraction method for aircraft air conditioning system health monitoring[J]. Chinese Journal of Aeronautics,2019,32(2):409-416.
[6]芮晓明,张穆勇,霍娟.基于性能可靠性的风电机组功率曲线评定新方法[J].动力工程学报,2014,34(9):742-747.
[7] TAUTZ-WEINERT J,WATSON S J. Using SCADA data for wind turbine condition monitoring–a review[J]. IET Renewable Power Generation,2017,11(4):382-394.
[8]杨天时.大型风电机组综合性能评估系统研究[D].北京:华北电力大学,2011.
[9]王志国,马一太,杨昭,等.风力发电机组性能分析的模糊综合评判方法[J].太阳能学报,2004,25(2):177-181.
[10] LAPIRA E,BRISSET D,ARDAKANI H D,et al.Wind turbine performance assessment using multiregime modeling approach[J]. Renewable Energy,2012,45:86-95.
[11]娄建楼,陆恒,胥佳,等.基于贝兹曲线的风机运行状态监测模型的研究[J].科技创新与应用,2016(8):10-11.
[12]杨茂,杨琼琼.风电机组风速-功率特性曲线建模研究综述[J].电力自动化设备,2018,32(2):34-43.
[13] LELLIS M D,REGINATTO R,SARAIVA R,et al.The Betz limit applied to airborne wind energy[J].Renewable Energy,2018,127.
[14]陈警钰,陈玉航.基于INNER-DBSCAN和功率曲线模型的风机异常状态检测[J].电力科学与工程,2017,33(8):27-34.
[15]赵永宁,叶林,朱倩雯.风电场弃风异常数据簇的特征及处理方法[J].电力系统自动化,2014,38(21):39-46.
[16]张鑫淼.基于SCADA数据的风电机组性能分析及健康状态评估[D].北京:华北电力大学,2017.
[17]苏勋文,米增强,陈盈今,等.基于运行数据的风电机组建模方法[J].电力系统保护与控制,2010,38(9):50-54.
[18]齐霞,安磊,张妍,等.风电机组功率曲线建模方法对比研究[J].可再生能源,2018,36(4):580-585.
[19]包道日娜,常征,代文平,等.基于IEC标准的小型风力发电机功率特性测试数据处理方法的研究[J].可再生能源,2014,32(5):649-654.
[2] JIA X, JIN C, BUZZA M, et al. Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves[J].Renewable Energy,2016,99:1191-1201.
[3] ULUYOL O,PARTHASARATHY G,FOSLIEN W,et al. Power curve analytic for wind turbine performance monitoring and prognostics[C]//Annual conference of the prognostics and health management society.2011:1-8.
[4] ALEXANDROS B, NIKOS P, BABIS M, et al.Enabling condition-based maintenance decisions with proactive event-driven computing[J]. Computers in Industry,2018,100:173-183.
[5] JIANZHONG S,CHAOYI L I,CUI L,et al. A data-driven health indicator extraction method for aircraft air conditioning system health monitoring[J]. Chinese Journal of Aeronautics,2019,32(2):409-416.
[6]芮晓明,张穆勇,霍娟.基于性能可靠性的风电机组功率曲线评定新方法[J].动力工程学报,2014,34(9):742-747.
[7] TAUTZ-WEINERT J,WATSON S J. Using SCADA data for wind turbine condition monitoring–a review[J]. IET Renewable Power Generation,2017,11(4):382-394.
[8]杨天时.大型风电机组综合性能评估系统研究[D].北京:华北电力大学,2011.
[9]王志国,马一太,杨昭,等.风力发电机组性能分析的模糊综合评判方法[J].太阳能学报,2004,25(2):177-181.
[10] LAPIRA E,BRISSET D,ARDAKANI H D,et al.Wind turbine performance assessment using multiregime modeling approach[J]. Renewable Energy,2012,45:86-95.
[11]娄建楼,陆恒,胥佳,等.基于贝兹曲线的风机运行状态监测模型的研究[J].科技创新与应用,2016(8):10-11.
[12]杨茂,杨琼琼.风电机组风速-功率特性曲线建模研究综述[J].电力自动化设备,2018,32(2):34-43.
[13] LELLIS M D,REGINATTO R,SARAIVA R,et al.The Betz limit applied to airborne wind energy[J].Renewable Energy,2018,127.
[14]陈警钰,陈玉航.基于INNER-DBSCAN和功率曲线模型的风机异常状态检测[J].电力科学与工程,2017,33(8):27-34.
[15]赵永宁,叶林,朱倩雯.风电场弃风异常数据簇的特征及处理方法[J].电力系统自动化,2014,38(21):39-46.
[16]张鑫淼.基于SCADA数据的风电机组性能分析及健康状态评估[D].北京:华北电力大学,2017.
[17]苏勋文,米增强,陈盈今,等.基于运行数据的风电机组建模方法[J].电力系统保护与控制,2010,38(9):50-54.
[18]齐霞,安磊,张妍,等.风电机组功率曲线建模方法对比研究[J].可再生能源,2018,36(4):580-585.
[19]包道日娜,常征,代文平,等.基于IEC标准的小型风力发电机功率特性测试数据处理方法的研究[J].可再生能源,2014,32(5):649-654.