基于改进劣化度模型的风电机组日常运行状态评估

金晓航; 秦治伟; 郭远晶; 单继宏

doi:10.19912/j.0254-0096.tynxb.2021-0830

PDF(2016 KB)

太阳能学报 ›› 2023, Vol. 44 ›› Issue (1) : 239-246. DOI: 10.19912/j.0254-0096.tynxb.2021-0830

基于改进劣化度模型的风电机组日常运行状态评估

金晓航^1~3, 秦治伟¹, 郭远晶⁴, 单继宏^1,2

作者信息 +

EVALUATION OF WIND TURBINES DAILY OPERATION CONDITIONS BASED ON IMPROVED DEGRADATION MODEL

Jin Xiaohang^1-3, Qin Zhiwei¹, Guo Yuanjing⁴, Shan Jihong^1,2

Author information +

文章历史 +

摘要

为准确地掌握整台风电机组的运行状态,提出一种基于改进劣化度模型的评估方法。首先求得风电机组层次结构中各状态参数的劣化度;其次通过组合赋权法确定各参数的权重,利用岭型分布隶属度函数确定各参数的隶属度矩阵;最后利用模糊综合评判法和彩色图谱对机组的运行状态进行评估和展示。案例机组的日常运行状态评估表明：所提方法可较SCADA系统提前发现机组发电机非驱动端轴承的异常,且误报次数较少。

Abstract

An improved degradation model is proposed to understand the operation conditions of wind turbines. Firstly, the degradation degree of each parameter in the hierarchical structure of wind turbine is calculated. Secondly, the weights of each state parameter and the parameter matrix are determined by an integrated method and the ridge type membership function respectively. Finally, the fuzzy comprehensive evaluation method and color images are used to evaluate and display the operating status of wind turbines. Results shows that the proposed method can detect the abnormal bearing at the non-drive end of wind turbine generator before the SCADA system, and the number of false alarms is less.

导出引用

金晓航, 秦治伟, 郭远晶, 单继宏. 基于改进劣化度模型的风电机组日常运行状态评估[J]. 太阳能学报. 2023, 44(1): 239-246 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0830

Jin Xiaohang, Qin Zhiwei, Guo Yuanjing, Shan Jihong. EVALUATION OF WIND TURBINES DAILY OPERATION CONDITIONS BASED ON IMPROVED DEGRADATION MODEL[J]. Acta Energiae Solaris Sinica. 2023, 44(1): 239-246 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0830

中图分类号： TK83

参考文献

[1] Global Wind Energy Council(GWEC)[EB/OL]. Global wind report2021 (2021-03) [2021-06-28]. https://gwec.net/ global-wind-report-2021/.
[2] 金晓航, 王宇, ZHANG B.工业大数据驱动的故障预测与健康管理[J]. 计算机集成制造系统, 2022, 28(5): 1314-1336.
JIN X H, WANG Y, ZHANG B.Industrial big data-driven fault prognostics and health management[J]. Computer integrated manufacturing systems, 2022, 28(5): 1314-1336.
[3] 黄玲玲, 符杨, 任浩瀚, 等. 基于状态信息的风电机组维护研究综述[J]. 中国电机工程学报, 2020, 40(21): 7065-7078.
HUANG L L, FU Y, REN H H, et al, Review of wind turbine maintenance based on condition monitoring systems[J]. Proceedings of the CSEE, 2020, 40(21): 7065-7078.
[4] 金晓航, 孙毅, 单继宏, 等. 风电机组故障诊断与预测技术研究综述[J]. 仪器仪表学报, 2017(5): 1042-1053.
JIN X H, SUN Y, SHAN J H, et al.Fault diagnosis and prognosis for wind turbine: an overview[J]. Chinese journal of scientific instrument, 2017(5): 1042-1053.
[5] 黄必清, 何焱, 王婷艳. 基于模糊综合评价的海上直驱风电机组运行状态评估[J]. 清华大学学报(自然科学版), 2015, 55(5): 543-549.
HUANG B Q, HE Y, WANG T Y.Fuzzy synthetic evaluation of the operational status of offshore direct-drive wind turbines[J]. Journal of Tsinghua University (science and technology), 2015, 55(5): 543-549.
[6] 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 Q, XU Q P, LI J, 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.
[7] 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 Y G, LI H, LIU H T, et al.Evaluation of health status of wind turbine based on multiple evidence method[J]. Acta energiae solaris sinica, 2018, 39(2): 331-341.
[8] 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 X H, ZHANG X M, ZHENG K, 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.
[9] 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 Y Q, WANG K P, HE G J, 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.
[10] 徐伟, 宋亚丽. 基于模糊层次分析法的光伏建筑综合效益评价[J]. 太阳能学报, 2018, 39(2): 544-549.
XU W, SONG Y L.Comprehensive benefits evaluation for PV building based on fuzzy analytic hierarchy process[J]. Acta energiae solaris sinica, 2018, 39(2): 544-549.
[11] 杨锡运, 刘欢, 张彬, 等. 组合权重相似日选取方法及光伏输出功率预测[J]. 电力自动化设备, 2014, 34(9): 118-122.
YANG X Y, LIU H, ZHANG B, et al.Similar day selection based on combined weight and photovoltaic power output forecasting[J]. Electric power automation equipment, 2014, 34(9): 118-122.
[12] YANG W X, COURT R, JIANG J K.Wind turbine condition monitoring by the approach of SCADA data analysis[J]. Renewable energy, 2013, 53(9): 365-376.
[13] JIN X H, XU Z W, QIAO W.Condition monitoring of wind turbine generators using SCADA data analysis[J]. IEEE transactions on sustainable energy, 2021, 12(1):202-210.
[14] 张玉玲, 迟国泰, 祝志川. 基于变异系数-AHP的经济评价模型及中国十五期间实证研究[J]. 管理评论, 2011, 23(1): 3-13.
ZHANG Y L, CHI G T, ZHU Z C.The economic evaluation model based on the coefficient of variation and AHP and empirical research during the 10th Five-Year of China[J]. Management review, 2011, 23(1): 3-13.
[15] SUN K, GAO J M, GAO Z Y, et al. Plant-wide quantitative assessment of a process industry system’s operating state based on color-spectrum[J]. Mechanical systems and signal processing, 2015, 60/61: 644-655.