基于云模型的风电机组输出功率特性分析
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摘要
风电机组性能的优劣直接影响着风电场安全生产和经济效益。输出功率是风电机组最重要、最具代表性的性能指标之一,风功率曲线是机组发电能力最直观的表述。以输出功率和风速为数据源,应用云模型特征量研究风电机组输出功率的波动特性,有利于掌握风电机组性能状态。在对风电机组SCADA系统风速、功率数据筛选的基础上,描绘风电机组正常工作状态下的风功率散点图,采用比恩法建立风电机组实际风功率曲线;统计分析不同风速区间的输出功率,利用逆向云发生器建立不同风速下的输出功率云模型,得到不同机组的整体功率云;通过对比分析功率云的特征值,实现输出功率大小、波动范围和离散程度的量化分析;同时计算风速、功率相关系数反映和评价机组响应的灵敏度。云模型的应用,把机组状态从定性评价拓展到定量评价,从宏观综合评价深入到风速区间段精准评价,提高了风电机组性能分析的准确性和全面性。最后,应用实例验证了算法的有效性和可靠性。
The performance of the wind turbine has a direct impact on the safety production and economic benefit of the wind farm. The output power is one of the most important and representative performance indexes of the wind turbine. The wind power curve is the most direct expression of its electricity-generating capacity. Using the output power and wind speed as the data source, the characteristic value of cloud model is adopted to study the fluctuation characteristics of output power, which is beneficial to learning the production status of the wind turbine. Based on the data sieving of the wind speed and power data collected by wind turbine SCADA system, a scatter plot is drawn to describe the wind speed and power in the normal working state of the wind turbine, and the actual wind power curve of wind turbine is established by method of bins. The overall power cloud of different units can be obtained through the statistical analysis of the output power of different wind speed ranges and the output power cloud model of different wind speeds that is constructed by the reverse cloud generator. The output power size, the fluctuation range and the degree of dispersion are quantitatively analyzed through comparing the characteristic value of the power cloud. At the same time, the correlation coefficients of wind speed and power are calculated to reflect and evaluate the sensitivity of the wind turbine response. The application of cloud model allows the evaluation of unit state to develop from a qualitative one to a quantitative one, from a comprehensive macro assessment to a precise assessment based on different wind speed range segments. In this way, it improves the accuracy and comprehensiveness of wind turbine performance analysis. Lastly, an applied example is used to prove the effectiveness and reliability of the algorithm.
The performance of the wind turbine has a direct impact on the safety production and economic benefit of the wind farm. The output power is one of the most important and representative performance indexes of the wind turbine. The wind power curve is the most direct expression of its electricity-generating capacity. Using the output power and wind speed as the data source, the characteristic value of cloud model is adopted to study the fluctuation characteristics of output power, which is beneficial to learning the production status of the wind turbine. Based on the data sieving of the wind speed and power data collected by wind turbine SCADA system, a scatter plot is drawn to describe the wind speed and power in the normal working state of the wind turbine, and the actual wind power curve of wind turbine is established by method of bins. The overall power cloud of different units can be obtained through the statistical analysis of the output power of different wind speed ranges and the output power cloud model of different wind speeds that is constructed by the reverse cloud generator. The output power size, the fluctuation range and the degree of dispersion are quantitatively analyzed through comparing the characteristic value of the power cloud. At the same time, the correlation coefficients of wind speed and power are calculated to reflect and evaluate the sensitivity of the wind turbine response. The application of cloud model allows the evaluation of unit state to develop from a qualitative one to a quantitative one, from a comprehensive macro assessment to a precise assessment based on different wind speed range segments. In this way, it improves the accuracy and comprehensiveness of wind turbine performance analysis. Lastly, an applied example is used to prove the effectiveness and reliability of the algorithm.
引文
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[15]蔡国伟,张斌,王建元,等.云模型理论在互联电力系统负荷频率控制中的应用[J].中国电机工程学报,2015,35(2):189-194.CAI Guowei,ZHANG Bin,WANG Jianyuan,et al.Application of cloud model theory to the load frequency control in interconnected power systems[J].Proceedings of the CSEE,2015,35(2):189-194.
[16]刘常昱,冯芒,戴晓军,等.基于云X信息的逆向云新算法[J].系统仿真学报,2004,16(11):2417-2420.LIU Changyu,FENG Mang,DAI Xiaojun,et al.A new algorithm of backward cloud[J].Journal of System Simulation,2004,16(11):2417-2420.
[17]JIA Xiaodong,CHAO Jin,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.
[2]黎孟岩,刘兴杰,米增强.风力发电机组功率曲线建模方法研究[J].云南电力技术,2012,40(3):1-5.LI Mengyan,LIU Xingjie,MI Zengqiang.Study on the modeling of wind turbine power[J].Yunnan Electric Power,2012,40(3):1-5.
[3]申新贺,潘东浩,唐继光.大型风电机组功率曲线的分析与修正[J].应用能源技术,2009(8):28-33.SHEN Xinhe,PAN Donghao,TANG Jiguang.Analysis and modification method for wind turbine power curve[J].Applied Energy Technology,2009(8):28-33.
[4]LAPIRA E,BRISSET D,ARDAKANI H D,et al.Wind turbine performance assessment using multi-regime modelinigapproach[J].Renewable Energy,2012,45:86-95.
[5]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.RUI Xiaoming,ZHANG Muyong,HUO Juan.A new performance reliability-based method for power curve assessment of wind turbines[J].Journal of Chinese Society of Power Engineering,2014,34(9):742-747.
[6]万书亭,万杰.基于量化指标和概率密度分布的风电功率波动特性研究[J].太阳能学报,2015,36(2):362-368.WAN Shuting,WAN Jie.Research on wind power fluctuation characteristics based on quantitative index and probability density distribution[J].Acta Energiae Solaris Sinica,2015,36(2):362-368.
[7]周松林,茆美琴,苏建徽.风电功率短期预测及非参数区间估计[J].中国电机工程学报,2011,31(25):10-16.ZHOU Songlin,MAO Meiqin,SU Jianhui.Short-term forecasting of wind power and non-parametric confidence interval estimation[J].Proceedings of the CSEE,2011,31(25):10-16.
[8]PARK J Y,LEE J K,OH K Y,et al.Development of a novel power curve monitoring method for wind turbines and its field tests[J].IEEE Transactions on Energy Conversion,2014,29(1):119-128.
[9]郎斌斌,穆钢,严干贵,等.联网风电机组风速—功率特性曲线的研究[J].电网技术,2008,32(12):70-74.LANG Binbin,MU Gang,YAN Gangui,et al.Research on wind speed vs output power characteristic curve of wind power generator interconnected with power grid[J].Power System Technology,2008,32(12):70-74.
[10]SKRIMPAS G A,SWEENE C W Y,MARHADI K S,et al.Employment of Kernel methods on wind turbine power performance assessment[J].IEEE Transactions on Sustainable Energy,2015,6(3):698-706.
[11]林鹏,赵书强,谢宇琪,等.基于实测数据的风电功率曲线建模及不确定估计[J].电力自动化设备,2015,35(4):90-95.LIN Peng,ZHAO Shuqiang,XIE Yuqi,et al.Wind power curve modeling based on measured data and uncertainty estimation[J].Electric Power Automation Equipment,2015,35(4):90-95.
[12]李德毅,杜鹢.不确定性人工智能[M].北京:国防工业出版社,2005.LI Deyi,DU Yi.Uncertainty artificial intelligence[M].Beijing:National Defense Industry Press,2005.
[13]马登昌.基于功率特性曲线的风电机组状态监测[D].北京:华北电力大学,2013.MA Dengchang.On-line monitoring of wind turbine based on thepower curves[D].Beijing:North China Electric Power University,2013.
[14]de ANDRADE V,SANZ-BOBI M A,KATO S.Wind turbine condition assessment based on changes observed in its power curve[C]//2013 International Conference on Renewable Energy Research and Applications(ICRERA),Madrid,2013:31-36.
[15]蔡国伟,张斌,王建元,等.云模型理论在互联电力系统负荷频率控制中的应用[J].中国电机工程学报,2015,35(2):189-194.CAI Guowei,ZHANG Bin,WANG Jianyuan,et al.Application of cloud model theory to the load frequency control in interconnected power systems[J].Proceedings of the CSEE,2015,35(2):189-194.
[16]刘常昱,冯芒,戴晓军,等.基于云X信息的逆向云新算法[J].系统仿真学报,2004,16(11):2417-2420.LIU Changyu,FENG Mang,DAI Xiaojun,et al.A new algorithm of backward cloud[J].Journal of System Simulation,2004,16(11):2417-2420.
[17]JIA Xiaodong,CHAO Jin,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.