随着风力机的装机容量和尺寸的不断增大，其运行环境更为复杂，疲劳问题突出，需要准确预测风力机结构遭受的实际疲劳损伤。通过对1.5MW风力机的塔底应变进行长期监测，研究了不同方法在处理雨流计数后残余数据点问题上的准确性。首先，通过采用不同的计算时窗，分析过渡循环对疲劳损伤的影响，选择的计算时窗越短，得到的疲劳损伤误差越大，计算时窗为10min时，误差达到了20.7%。然后，使用残波串联法处理残余数据点，此时选择不同的计算时窗得到的损伤值均与将监测时段内所有数据作为连续序列得到的结果相同，证明了该方法在处理雨流残余数据时的准确性。该成果为风力机结构疲劳损伤在线预测提供了解决方法，可以更加准确的得到构件的实时损伤。
Abstract
With the increasing of the installed capacity and dimension of the wind turbine, its operating environment is more complex and the fatigue problem is prominent. So it is necessary to accurately predict the actual fatigue damage suffered by the wind turbine structure. Through long-term monitoring of tower bottom strain of 1.5MW wind turbine, the accuracy of different methods in dealing with residue series after rainflow counting is studied. First, the influence of transition cycles on fatigue damage was studied using different window lengths. The shorter the selected window length is, the greater the fatigue damage error is. When the window length was 10min, the error reached 20.7%. Then, residue concatenation methodology was used to process residue series. At this time, the damage values obtained by selecting different window lengths were the same as those obtained by taking all data in the monitoring period as continuous series, which proved the accuracy of this method in processing residue series. The results provide a solution for the online prediction of fatigue damage of wind turbine structures, and can obtain the fatigue damage in real time more accurately.
关键词
风力机 /
疲劳损伤 /
雨流计数法 /
残波 /
过渡循环 /
残波串联法
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Key words
wind turbine /
fatigue damage /
rainflow counting method /
residue series /
transition cycles /
residue concatenation methodology
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参考文献
[1] 吴琪强, 郭帅平, 王钢, 等. 基于固有频率的风力机叶片裂纹精确定位与程度识别[J]. 振动与冲击, 2019, 38(24): 18-27.
WU Qiqiang, GUO Shuaiping, WANG Gang, et al. Accurate location and degree identification of wind turbine blade cracks based on natural frequency[J]. Journal of Vibration and Shock, 2019, 38(24): 18-27.
[2] IEC 61400-1. Wind turbines-Part 1: Design requirements[S]. Genève: Switzerland. 2005: 33-39.
[3] DNVGL-RP-C203. Fatigue design of offshore steel structures[S]. Norway: Det Norske Veritas , 2016.
[4] Germanischer Lloyd WindEnergie GmbH. Recommendations for design of offshore wind turbines [S]. Deliverable D1 – external conditions, state of the art, 2003.
[5] STAVRIDOU N, EFTHYMIOU E, BANIOTOPOULOS C C. Welded connections of wind turbine towers under fatigue loading: finite element analysis and comparative study[J]. American Journal of Engineering and Applied Sciences, 2015, 8(4): 489-503.
[6] 胡格吉乐吐. 基于风振效应下的风机塔架门洞数值优化[D]. 呼和浩特: 内蒙古工业大学, 2015.
HU Gejiletu. Numerical optimization of wind turbine tower gateway based on wind vibration effect[D] Hohhot: Inner Mongolia University of Technology, 2015.
[7] YU Z L, SUN P W, WANG D. Fatigue life prediction for flange connecting bolts of wind turbine tower[J]. Journal of Shanghai Jiaotong University: science, 2020, 4: 526-530.
[8] CURRIE M, SAAFI M, TACHTATZIS C, et al. Structural integrity monitoring of onshore wind turbine concrete foundations[J]. Renewable Energy, 2015, 83: 1131-1138.
[9] 向东, 蒋李, 沈银华, 等. 风电齿轮箱在随机风载下的疲劳损伤计算模型[J]. 振动与冲击, 2018, 37(11): 115-123.
XIANG Dong, JIANG Li, SHEN Yinhua, et al. Fatigue damage calculation model for wind turbine gearboxes under random wind loads[J]. Journal of Vibration and Shock, 2018, 37(11): 115-123.
[10] YE X W, NI Y Q, KO J M. Experimental evaluation of stress concentration factor of welded steel bridge T-joints[J]. Journal of Constructional Steel Research, 2012, 70 (2): 78-85.
[11] WEIJTENS W, NOPPE N, VERBELEN T, et al. Offshore wind turbine foundation monitoring, extrapolating fatigue measurements from fleet leaders to the entire wind farm[J]. Journal of Physics Conference Series, 2016, 753(9): 092018.
[12] 赵艳. 风机结构振动监测及动力响应研究[D]. 杭州: 浙江大学, 2018.
ZHAO Yan. Vibration monitoring and dynamic response analysis of a wind turbine[D]. Hangzhou: Zhejiang University, 2018.
[13] LORAUX C, BRUHWILER E. Realistic examination of the fatigue life of a wind turbine tower using data from long term monitoring[C]//IABSE Conference – Structural Engineering: Providing Solutions to Global Challenges. Geneva, Switzerland, 2015.
[14] MARSH G. Fatigue load monitoring of offshore wind turbine support structures[D]. Glasgow : University of Strathclyde, 2016.
[15] SUTHERLAND H. J. On the fatigue analysis of wind turbines[M]. Sandia National Laboritories, 1999.
[16] LARSEN G C, THOMSEN K., A simple approximative procedure for talking into account low cycle fatigue loads[C]//Proceedings of. IEA 4th Symposium of Wind Turbine Fatigue, Stuttgart, 1996.
[17] MOUZAKIS F, MORFIADAKIS E. Identification of low cycle effects on wind turbine component lifetime estimation[R]. British Wind Energy Association, 1997.
[18] AMZALLAG C, GEREY JP, ROBERT J L, et al. Standardization of the rainflow counting method for fatigue analysis[J]. International Journal of Fatigue, 1994;16: 287–293.
[19] ASTM E1049-85. Standard practices for cycle counting in fatigue analysis, Reapproved 2011.
[20] 韩为铎, 卢剑伟, 莫家奇. 非线性渐变刚度钢板弹簧疲劳分析载荷谱编制方法[J]. 振动与冲击, 2017, 36(6): 241-245.
HAN Weiduo, LU Jianwei, MO Jiaqi. Load spectrum compilation method for the fatigue analysis of leaf springs with nonlinear varying stiffness[J]. Journal of Vibration and Shock, 2017, 36(6): 241-245.
[21] MATSUISHI M, ENDO T. Fatigue of metals subjected to varying stress[J]. Fukuoka: Japan Society of Mechanical Engineers; 1968.
[22] BANNANTINE J A, COMER J J, HANDROCK J L. Fundamentals of metal fatigue analysis[M]. Englewood Cliffs (NJ): Prentice-Hall; 1990.
[23] MCLNNES C H, MEEHAN P A. Equivalence of four-point and three-point rainflow cycle counting algorithms[J]. International Journal of Fatigue, 2008, 30: 547–559.
[24] 赵艳, 杜艳强, 王新武, 等. 风力机支撑结构疲劳损伤评估中的不确定性分析[EB/OL].( 2022-05-05)[2022-12-19].https://doi.org/10.13334/j.0258-8013.pcsee.220871.
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