Research on Unified Assessment Model of Wind Turbine Efficiency and Performance

doi:10.3901/JME.2021.14.253

Abstract

Abstract: The unstable and uncertain changes of wind speed and direction make it difficult for wind turbines(WTs) to maintain a stable wind energy transition state, which directly leads to the uncertainty of power generation state of WTs. Accurately assessing the two states of energy absorption and production capacity of the wind turbines is of great significance for the formulating production, scheduling strategies and maintenance decisions. By analyzing the energy conversion process of the wind turbines, it is pointed out that the wind turbine has two existing states:explicit and implicit, and further study the two functional state characteristics of efficiency and performance of wind turbine. The cloud model is used to describe the wind turbine monitoring data, and the cloud characteristic parameters are used to quantitatively describe the expected characteristics, deviated characteristics and discrete characteristics of the unit's implicit state. The evaluation model of implicit state for wind turbines, which is embodied in Supervisory control and data acquisition(SCADA) data and brings together wind turbine's efficiency and performance, is established. The partial evaluation of the different wind speeds sections and different power parameter sections of the unit and the overall comprehensive evaluation of the entire production parameter sections are realized. The evaluation results can provide basis for production scheduling and maintenance decisions, and promote wind farm science and optimization management. Finally, the wind farm field monitoring data is used to verify the correctness and reliability of the algorithm.

Key words: wind turbines, implicit state, efficiency cloud, performance cloud, quantitative assessment

CLC Number:

TM614

DONG Xinghui, LI Jia, GAO Di, ZHENG Kai. Research on Unified Assessment Model of Wind Turbine Efficiency and Performance[J]. Journal of Mechanical Engineering, 2021, 57(14): 253-260.

References

[1] 张帆,刘德顺,戴巨川,等. 一种基于SCADA参数关系的风电机组运行状态识别方法[J]. 机械工程学报,2019,55(4):1-9.ZHANG Fan,LIU Deshun,DAI Juchuan,et al. An operating condition recognition method of wind turbine based on SCADA parameter relations[J]. Journal of Mechanical Engineering,2019,55(4):1-9.
[2] KUSIAK A,ANDRE W,ANOOP V. Monitoring wind farms with performance curves[J]. IEEE Transactions on Sustainable Energy,2013,4(1):192-199.
[3] 霍娟,唐贵基,张穆勇,等. 基于输出功率波动性的风电机组性能分析方法[J]. 可再生能源,2015,33(12):1809-1815.HUO Juan,TANG Guiji,ZHANG Muyong,et al. Analysis method of wind turbine performance based on output power fluctuation[J]. Renewable Energy Resources,2015,33(12):1809-1815.
[4] DAI Shuoming. Analysis of power generation performance evaluation methods for wind turbines[J]. Technological Development of Enterprise,2016,35(9):95-96.
[5] 付立,刘晓光. 基于激光雷达测风仪的风电场风电机组性能评估研究[J]. 华电技术,2017,39(6):14-16,40,77.FU Li,LIU Xiaoguang. Wind power plant wind turbine unit performance evaluation research based on laser radar anemoscope Huadian[J]. Technology,2017,39(6):14-16,40,77.
[6] 兰维,张中泉,雷阳,等. 风电机组出力性能分析方法研究[J]. 发电与空调,2017,38(3):26-29,46.LAN Wei,ZHANG Zhongquan,LEI Yang,et al. Study on output performance analysis of wind turbine[J]. Power Generation & Air Condition,2017,38(03):26-29,46.
[7] LAPIRA E,BRISSET D,ARDAKANI H D,et al. Wind turbine performance assessment using multi-regime modeling approach[J].Renewable Energy,2012(45):86-95.
[8] 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. WANG Zhiguo,MA Yitai,YANG Zhao,et al. Fuzzy comprehensive evaluation method of wind power generation unit[J]. Acta Energiae Solaris Sinica,2004,25(2):177-181.
[9] YANG Wenxian,COURT R,JIANG Jiesheng. Wind turbine condition monitoring by the approach of SCADA data analysis[J]. Renewable Energy,2013,53:365-376.
[10] 叶小广. 风电场发电性能评价与风电机组数据分析方法研究[J]. 电力设备管理,2018(3):66-69.YE Xiaoguang. Research on wind farm power generation performance evaluation and wind turbine data analysis method[J]. Electric Power Equipment Managemnt,2018(3):66-69.
[11] 任杰. 大型风电机组运行状态评价与分析[D]. 北京:华北电力大学,2014.REN Jie. Running status evaluation and analysis for large-scale wind Turbines[D]. Beijing:North China Electric Power University,2014.
[12] 万杰. 基于SCADA数据的风电机组运行状态评估方法研究[D]. 北京:华北电力大学,2014.WAN Jie. Wind turbines running condition assessment method researched by SCADA data[D]. Beijing:North China Electric Power University,2014.
[13] 梁颖,方瑞明. 基于SCADA和支持向量回归的风电机组状态在线评估方法[J]. 电力系统自动化,2013,37(14):7-12,31.LIANG Ying,FANG Ruiming. An online wind turbine condition assessment method based on SCADA and suport vector regression[J]. Automation of Electric Power Systems,2013,37(14):7-12,31.
[14] 姜漫利. 基于大数据分析的风电机组运行状态建模与监测[D]. 北京:华北电力大学,2017.JIANG Manli. Wind turbine condition modeling and monitoring based big data[D]. Beijing:North China Electric Power University,2017.
[15] 李娇娇. 基于粗集-神经网络的风电机组状态评估及齿轮箱异常预警[D]. 泉州:华侨大学,2014.LI Jiaojiao. The research on condition assessment of wind turbine and early warning of gearbox based on roughset & aritificial neural network[D]. Quanzhou:Huaqiao University,2014.
[16] 李辉,胡姚刚,唐显虎,等. 并网风电机组在线运行状态评估方法[J]. 中国电机工程报,2010,30(33):103-109.LI Hui,HU Yaogang,TANG Xianhu,et al. Method for on-line operating conditions assessment for a grid-connected wind turbine generator system[J]. Proceedings of the CSEE,2010,30(33):103-109.
[17] LI S,WUNSCH D C,HAIR E O',et al. Compararive analysis of regression and artificial neural network models for wind turbine power curve estimation[J]. Journal of Solar Energy Engineering,2001,123(4):327-331.
[18] USTUNTAS T,SAHIN A D. Wind turbine power curve estimation based on cluster center fuzzy logic modeling[J]. Journal of Wind Engineering and Industrial Aerodynamics,2008,96(5):611-620.
[19] JIA Xiaodong,JIN Chao,MATT B,et al. Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves[J]. Renewable Energy,2016,99:1191-1201.
[20] 董文婷. 基于大数据分析的风电机组健康状态的智能评估及诊断[D]. 上海:东华大学,2016.DONG Wenting. Intelligent evaluation and diagnosis of the health operational condition on wind turbines based on big data analysis[D]. Shanghai:Donghua University,2016.
[21] 肖运启,王昆朋,贺贯举,等. 基于趋势预测的大型风电机组运行状态模糊综合评价[J]. 中国电机工程学报,2014,34(13):2132-2139.XIAO Yunqi,WANG Kunming,HE Guanju,et al. Fuzzy comprehensive evaluation for operating condition of large-scale wind turbines based on trend predication[J]. Proceedings of the CSEE,2014,34(13):2132-2139.
[22] 杨光. 大型风电机组故障诊断与状态综合评价方法研究[D]. 北京:华北电力大学,2017.YANG Guang. Research on fault diagnosis and state comprehensive evaluation method for large-scale wind turbine[D]. Beijing:North China Electric Power University,2017.
[23] 李辉,胡姚刚,唐显虎,等. 并网风电机组在线运行状态评估方法[J]. 中国电机工程报,2010,30(33):103-109.LI Hui,HU Yaogang,TANG Xianhu,et al. Method for on-line operating conditions assessment for a grid-connected wind turbine generator system[J]. Proceedings of the CSEE,2010,30(33):103-109.
[24] 周进. 基于运行数据的风电机组性能评估研究[D]. 北京:华北电力大学,2015.ZHOU Jin. Research on performance evaluation of wind turbine based on operation data[D]. Beijing:North China Electric Power University,2015.
[25] 董玉亮,李亚琼,曹海斌,等. 基于运行工况辨识的风电机组健康状态实时评价方法[J]. 中国电机工程学报,2013,33(11):88-95,15.DONG Yuliang,LI Yaqiong,CAO Haibin,et al. Real-time health condition evaluation on wind turbines based on operational condition recognition[J]. Proceedings of the CSEE,2013,33(11):88-95,15.
[26] 熊浩,孙才新,杜鹏,等. 基于物元理论的电力变压器状态综合评估[J]. 重庆大学学报,2006(10):24-28.XIONG Hao,SUN Caixin,DU Peng,et al. Synthetic assessment of power transformer condition based on matter element theory[J]. Journal of Chongqing University,2006(10):24-28.
[27] 迟冰. 基于运行数据的风电机组在线状态评估[D]. 北京:华北电力大学,2016.CHI Bing. Wind turbine online condition assessment based on operation data[D]. Beijing:North China Electric Power University,2016.
[28] 王森,仉志华,薛永端,等. 风电场多状态出力的概率性评估[J]. 太阳能学报,2016,37(6):1611-1616.WANG Sen,JI Zhihua,XUE Yongrui,et al. Probabilistic assessment for multi-state output of wind farm[J]. Acta Energiae Solaris Sinica,2016,37(6):1611-1616.
[29] 王昕伟,张建华,蒋程,等. 基于序贯蒙特卡罗方法的风电场有功出力可靠性评估[J]. 太阳能学报,2014,35(1):106-112.WANG Xinwei,ZHANG Jianhua,JIANG Cheng,et al. Reliability assessment of wind farm active power based on sequential monte-carlo method[J]. Acta Energiae Solaris Sinica,2014,35(1):106-112.
[30] 郭建英,孙永全,王铭义,等. 基于计算机仿真的风电机组系统可靠性综合[J]. 机械工程学报,2012, 48(2):2-8.GUO Jianying,SUN Yongquan,WANG Mingyi,et al. System reliability synthesis of wind turbine based on computer simulation[J]. Journal of Mechanical Engineering,2012,48(2):2-8.
[31] 周湶,徐清鹏,李剑,等. 融合集对分析和证据理论的风电机组运行状态评估[J]. 电力自动化设备,2017,37(7):38-45.ZHOU Quan,XU Qingpeng,LI Jian,et al. Operating state assessment based on set-pair analysis and evidential reasoning decision-making for wind turbine generator unit[J]. Electric Power Automation Equipment,2017,37(7):38-45.
[32] 尹传涛. 风电机组能效评价与诊断研究[D]. 北京:华北电力大学,2017.YIN Chuantao. Energy efficiency evaluation and diagnosis of wind turbine[D]. Beijing:North China Electric Power University,2017.
[33] 胡姚刚,李辉,刘海涛,等. 基于多类证据体方法的风电机组健康状态评估[J]. 太阳能学报,2018,39(2):331-341.HU Yaogang,LI Hui,LIU Haitao,et al. Evaluation of health status of wind turbine based on multiple evidence method[J]. Acta Energiae Solaris Sinica,2018,39(2):331-341.
[34] 董兴辉,张鑫淼,张光,等. 基于云模型的风电机组输出功率特性分析[J]. 机械工程学报,2017,53(22):198-205.DONG Xinghui,ZHANG Xinmiao,ZHANG Guang,et al. Analysis of wind turbine output power characteristic based on cloud model[J]. Journal of Mechanical Engineering,2017,53(22):198-205.
[35] 彭建刚,刘明周,张铭鑫,等. 基于改进非支配排序的云模型进化多目标柔性作业车间调度[J]. 机械工程学报,2014,50(12):198-205.PENG Jiangang,LIU Mingzhou,ZHANG Mingxin, et al. Cloud model evolutionary multi-objective flexible job-shop scheduling based on improved non-dominated sorting[J]. Journal of Mechanical Engineering,2014,50(12):198-205.
[36] 董兴辉,张鑫淼,郑凯,等. 基于组合赋权和云模型的风电机组健康状态评估[J]. 太阳能学报,2018,39(8):2139-2146.DONG Xinghui,ZHANG Xinmiao,ZHENG Kai,et al. Health status assessment of wind turbine based on combination weighting and cloud model[J]. Acta Energiae Solaris Sinica,2018,39(8):2139-2146.
[37] DONG Xinghui,LI Jia,GAO Di,et al. Wind speed modeling for cascade clusters of wind turbines Part 1:The cascade clusters of wind turbines[J]. Energy,2020,205:118097.
[38] DONG Xinghui,LI Jia,GAO Di,et al. Wind speed modeling for cascade clusters of wind turbines Part 2:Wind speed reduction and aggregation superposition[J]. Energy,2021,215:119145.