中文版 | English
题名

基于Kalman 滤波海上风机结构健康监测 研究

其他题名
STUDIES ON STRUCTURAL HEALTH MONITORING OF OFFSHORE WIND TURBINES BASED ON KALMAN FILTER
姓名
姓名拼音
XIE Yuanchun
学号
11930327
学位类型
硕士
学位专业
0801 力学
学科门类/专业学位类别
08 工学
导师
安松
导师单位
前沿与交叉科学研究院
论文答辩日期
2022-05-09
论文提交日期
2022-06-13
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

大型土木工程结构的疲劳分析和结构健康监测日益成为关注焦点,如
何通过有限个传感器测量数据实现结构整体动力响应估计和结构外载荷估
计成为一项具有挑战性的任务。随着海上风电的日益发展,由于海上风机
纤长的结构特点和复杂、具有一定周期性的风、浪、流环境载荷,使得海
上风机结构的疲劳分析和结构健康监测成为重要课题。
本文主要提出了用于海上风机结构整体响应估计与分布载荷识别的算
法,该算法目的是实现风机整体结构动力重构,为疲劳分析和结构健康监
测提供依据。
本文首先分析了引起风机结构振动的主要环境载荷及其计算方法、振
动理论和基于Kalman 滤波的Joint input-state estimation 算法,之后在该算
法的基础上,提出了分两步实现结构整体动力响应估计和分布载荷估计的
算法。由于到海上风机常见的环境载荷多为分布载荷,新算法首先将适用
于多个集中力载荷的原算法推广至分布载荷情形, 提出了模态等效力法用
于估计结构整体动力响应,悬臂梁的数值算例结果验证了模态等效力法的
有效性。然后分析了海洋环境载荷的时空解耦特性,在已经估计得到的模
态等效力基础上, 提出了利用切比雪夫多项式近似分布载荷分布函数的方
法,该方法通过有限元模型下分布载荷的比例特性,利用相互存在比例关
系的模态等效力实现分布函数的切比雪夫多项式近似,该算法成功运用于
平面悬臂梁和空间悬臂梁的数值算例中,通过多个传感器测量数据,得到
了悬臂梁整体的动力响应、分布载荷、固定支座反力较为满意的估计结果。
最后将算法运用于一个单桩式海上风机的数值计算中,利用Sesam 中的
Sima 对单桩式海上风机进行建模,并计算了6 种载荷组合情形下的结构动
力响应,并对模态等效力法进行了验证。

其他摘要

Fatigue analysis and structural health monitoring of large -scale civil
engineering structures have increasingly become the focus of attention . How to
estimate the global dynamic response and the load of the structure through the
measurement data from a limited number of sensors has become a challenging
task. With the increasing development of offshore wind turbine, the fatigue
analysis and structural health monitoring of offshore wind turbine structures has
become an important topic due to the slender structural characteristics of offshore
wind turbines and periodic wind, wave and current loads.
This paper mainly proposes an algorithm for the estimation of the global
response and distributed load of offshore wind turbine. The purpose of this
algorithm is to reconstruct the global dynamic response of the structure and
contribute to fatigue analysis and structural health monitoring.
This paper firstly analyzes the main environmental loads that cause the
vibration of the offshore wind turbine and loads calculation method, vibration
theory and the joint input-state estimation algorithm based on Kalman filter.
Based on the Joint input-state estimation algorithm, this paper proposes an
algorithm to estimate the global dynamic response and distributed load of the
structure in two steps. Considering that the environmental loads of offshore wind
turbines are mostly distributed loads, the new algorithm firstly extends the
original algorithm suitable for multiple concentrat ed force to the distributed load
case, and proposes modal equivalent force method to estimate the global dynamic
response of the structure. The numerical example of the cantilever beam verifies
the effectiveness of the modal equivalent force method. Then, by analyzing the
space-time decoupling characteristics of distributed loads, on the basis of the
estimated modal equivalent force, a method for approximating the distribution
function of distributed loads using Chebyshev polynomials is proposed . This
method mainly considers the proportional characteristics of the distributed load
under the finite element model, and gets the Chebyshev polynomial
approximation of the distribution function by using the modal equivalent forcesthat have a proportional relationship with each other. The algorithm was
successfully applied to the numerical examples of 2D cantilever beam and 3D
cantilever beam. Using the measurement data of multiple sensors, satisfactory
estimation results of the global dynamic response, distributed load and reaction
force of the cantilever beam are obtained. Finally, the algorithm is applied to a
numerical example of a monopile offshore wind turbine modeled by Sima
(Sesam). The dynamic responses of the structure under six load combinations are
calculated, and the modal equivalent force method is properly verified.

关键词
其他关键词
语种
中文
培养类别
独立培养
入学年份
2019
学位授予年份
2022-06
参考文献列表

[1] 姚中原.我国海上风电发展现状研究[J].中国电力企业管理,2019(22):24-28.
[2] 孙一琳.全球海上风电市场现状与展望[J].风能,2020(09):40-43.
[3] 白旭.中国海上风电发展现状与展望[J].船舶工程,2021,43(10):12-15.
[4] 刘超,徐跃.后疫情时代我国海上风电发展对策探究[J].中外能源,2021,26(03):14-19.
[5] ZHOU L, LI Y, LIU F, et al. Investigation of dynamic characteristics of a monopile wind turbine based on sea test[J]. Ocean Engineering, 2019, 189(Oct.1):106308.1 -106308.17.
[6] LIN Z, PENG XH, SHU KC, et al. Structural health monitoring of offshore wind power structures based on genetic algorithm optimization and uncertain analytic hierarchy process[J]. Ocean Engineering, 218.
[7] KEFAL A, TESSLER A, OTERKUS E. An enhanced inverse finite element method for displacement and stress monitoring of multilayered composite and sandwichstructures[J]. Composite Structures, 2017, 179(nov.):514 -540.
[8] BRINCKER R, VENTURA C E. Introduction to Operational Modal Analysis[M] . 2015.
[9] DEVRIENDT C, MAGALHÃES F, WEIJTJENS W, et al. Structural health monitoring of offshore wind turbines using automated operational modal analysis. Structural Health Monitoring. 2014;13(6):644 -659.
[10] LIU F, GAO S, HAN H, et al. Interference reduction of high-energy noise for modal parameter identification of offshore wind turbines based on iterative signal extraction[J]. Ocean Engineering, 2019, 183(JUL.1):372 -383.
[11] LIU F, GAO S, LIU D, et al. A signal decomposition method based on repeated extraction of maximum energy component for offshore structures[J]. MarineStructures, 2020, 72(1):102779.
[12] LIU F, GAO S, ZHE T A, et al. A new time -frequency analysis method based on single mode function decomposition for offshore wind turbines[J]. Marine Structures, 72.
[13] 董霄峰,练继建,王海军.海上风机结构振动监测试验与特性分析[J].天津大学学报:自然科学与工程技术版,2019, 52(2):9.
[14] KRAEMER P, FRIEDMANN H. Vibration-based structural health monitoring for offshore wind turbines - Experimental validation of stochastic subspace algorithms[J]. Wind & structures, 2015, 21(6 ):693-707.
[15] MOJTAHEDI A, HOKMABADY H, YAGHUBZADEH A, et al. An improved model reduction-modal based method for model updating and health monitoring of an offshore jacket-type platform[J]. Ocean Engineering, 2020, 209:107495.
[16] TESSLER A, SPANGLER J L. A Variational Principle for Reconstruction of ElasticDeformations in Shear Deformable Plates and Shells. 2003.
[17] TESSLER A, SPANGLER J. Inverse FEM for Full-Field Reconstruction of ElasticDeformations in Shear Deformable Plates and Shells[J]. 2003.
[18] GHERLONE M, CERRACCHIO P, MATTONE M, et al. Shape sensing of 3D frame structures using an inverse Finite Element Method[J]. International Journal of Solids & Structures, 2012, 49(22):3100 -3112.
[19] KEFAL A, ERKAN O, ALEXANDER T, et al. A quadrilateral inverse-shell element with drilling degrees of freedom for shape sensi ng and structural health monitoring [J],Engineering Science and Technology, 2016, 19(3),1299-1313.
[20] KEFAL A. An efficient curved inverse-shell element for shape sensing and structural health monitoring of cylindrical marine structures[J]. Ocean Engineering , 2019,188:106262-.
[21] PAPA U, RUSSO S, LAMBOGLIA A, et al. Health Structure Monitoring for the Design of an Innovative UAS fixed wing through Inverse Finite Element Method (iFEM)[J].Aerospace Science and Technology, 2017
[22] YONG Z, JING LD, HONG B, et al. Optimal Sensor Placement Based on Eigenvalues Analysis for Sensing Deformation of Wing Frame Using iFEM[J]. Sensors, 2018,18(8):2424-.
[23] Ml A, AKBCD E, EO A, et al. Structural health monitoring of an offshore wind turbine tower using iFEM methodology[J]. Ocean Engineering, 204.
[24] KEFAL A, ADNAN, OTERKUS E, et al. Displacement and stress monitoring of achemical tanker based on inverse finite element method[J] . Ocean Engineering, 2016.
[25] KEFAL A, OTERKUS E. Displacement and stress monitoring of a Panamaxcontainership using inverse finite element method[J]. Ocean Engineering, 2016,119(JUN.1):16-29.
[26] KEFAL, ADNAN, MAYANG, et al. Three dimensional shape and stress monitoring of bulk carriers based on iFEM methodology[J]. Ocean Engineering, 2018.
[27] AZAM S E, RAGEH A, LINZELL D. Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition[J]. Structural Control& Health Monitoring, 2019, 26(2):e2288.1-e2288.24.
[28] XBA B, TF A, CHEN S, et al. One-dimensional convolutional neural network for damage detection of jacket-type offshore platforms[J]. Ocean Engineering, 2020.
[29] ZIEGLER L, COSACK N, KOLIOS A, et al. Structural monitoring for lifetimeextension of offshore wind monopiles: Verif ication of strain-based load extrapolation algorithm[J]. Marine Structures, 2019, 66(JUL.):154-163.
[30] PASSON P, RASMUSSEN J H. Offshore Wind Turbine Foundation Design[D]. DTU Wind Energy PhD;2015;No.0044.
[31] PASSON P, BRANNER K. Load calculation methods for off shore wind turbinefoundations[J]. Ships & Offshore Structures, 2014, 9(4):433-449.
[32] MOYLAN P J. Stable inversion of linear systems[J]. IEEE Transactions on Automatic Control, 1977, 22(1):74-78.
[33] ALEXANDROS I, RASOUL S, WOUT W, et al. A modal decomposition andexpansion approach for prediction of dynamic responses on a monopile offshore wind turbine using a limited number of vibration sensors[J]. Mechanical Systems and Signal Processing, 2015, 68:84-104.
[34] ILIOPOULOS A, WEIJTJENS W, VAN HEMELRIJCK D, et al. Fatigue assessmentof offshore wind turbines on monopile foundations using multi -band modalexpansion[J]. Wind Energy, 2017, 20(8):1463-1479.
[35] NOPPE N, ILIOPOULOS A, WEIJTJENS W, et al. Full load estimation of an offshore wind turbine based on SCADA and accelerometer data[J]. Journal of PhysicsConference, 2016, 753:072025.
[36] GILLIJNS S, MOOR B D. Unbiased minimum-variance input and state estimation for linear discrete-time systems[J]. Automatica Oxford, 2007.
[37] GILLIJNS S, MOOR B D. Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough[J]. Automatica Oxford, 2007.
[38] MAES K, SMYTH A W, ROECK G D, et al. Joint input-state estimation in structural dynamics[J]. Mechanical Systems and Signal Processing, 2016.
[39] MAES K, ILIOPOULOS A, WEIJTJENS W, et al. Dynamic strain estimation forfatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms[J]. Mechanical Systems & Signal Processing, 2016, 76-77(aug.):592-611.
[40] MAES K, NIMMEN K V, LOURENS E, et al. Verification of joint input-stateestimation for force identification by means of in situ measurements on a footbridge[J].Mechanical Systems & Signal Processing, 2016, 75(Jun.):245-260.
[41] MAES K, KARLSSON F, LOMBAERT G. Tracking of inputs, states and parameters of linear structural dynamic systems[J]. Mechanical Systems and Signal Processing,2019, 130:755-775.
[42] GHALEB F, ZAINAL A, RASSAM M, et al. Improved vehicle positioning algorithm using enhanced innovation-based adaptive Kalman filter[J]. Pervasive and Mobile Computing, 2017, 40:139-155.
[43] ZHENG B, FU P, LI B, et al. A Robust Adaptive Unscented Kalman Filter forNonlinear Estimation with Uncertain Noise Covariance[J]. Sensors, 2018, 18(3):808.
[44] ZHANG XH, WU ZB. Dual-Type Structural Response Reconstruction Based onMoving-Window Kalman Filter with Unknown Measurement Noise[J]. Journal ofAerospace Engineering,2019,32(4):04019029.104019029.14.
[45] 张笑华,吴志彪,吴圣斌,黄梅萍.基于移动窗卡尔曼滤波算法的结构响应重构[J].振动与冲击,2021,40(21):90-96.
[46] DESSI D. Load field reconstruction with a combined POD and integral splineapproximation technique[J]. Mechanical Systems & Signal Processing, 2014,46(2):442-467.
[47] LI K, LIU J, Han X, et al. A novel approach for distributed dynamic loadreconstruction by space-time domain decoupling[J]. Journal of Sound and Vibration,2015.
[48] LIU J, LI K. Sparse identification of time-space coupled distributed dynamic load[J].Mechanical Systems and Signal Processing, 2021, 148(2):107177.
[49] WANG L, LIU Y. An inverse method for distributed dynamic load identification of structures with interval uncertainties[J]. Advances in Engineering Software, 2019,131:77-89.
[50] FALTINSEN O M. Sea Loads on Ships and Offshore Structures[M]. NEW YORK NY CAMBRIDGE UNIVERSITY PRESS, 1990.
[51] 吴望一.流体力学[M].北京:北京大学出版社, 1982.
[52] 邱大洪. 波浪理论及其在工程上的应用[M].北京:高等教育出版社,1985.
[53] DEAN R G. Stream function representation of nonlinear ocean waves[J]. Journal of Geophysical Research, 1965, 70(18).
[54] FENTON J D. The numerical solution of steady water wave problems[J]. Computers & Geosciences, 1988, 14(3):357-368.
[55] DNV-RP-C205. Environmental Conditions and Environmental Loads [S]. Det Norske Veritas AS.
[56] DNV-OS-J101. Design of Offshore Wind Turbine Structures [S]. Det Norske Veritas AS.
[57] DNV-OS-C101. Design of Offshore Steel Structures, General (LRFD Method) [S]. Det Norske Veritas AS.
[58] International Electro Technical Commission. Wind turbine generator systems(IEC61400-1)[S]. British Electrotechnical Committee.
[59] ARANY L, BHATTACHARYA S, MACDONALD J, et al. Simplified critical mudline bending moment spectra of offshore wind turbine support structures[J]. Wind Energy,2015, 18(12).
[60] ARANY L, BHATTACHARYA S, MACDONALD J, et al. Design of monopiles foroffshore wind turbines in 10 steps[J]. Soil Dynamics & Ear thquake Engineering, 2017,92:126-152.
[61] 中华人民共和国交通运输部.港口工程载荷规范(JTS144-1-2010)[S].北京:人民交通出版社,2010.
[62] 中国船级社.海上风力发电机组认证规范[S].北京:人民交通出版社,2012.
[63] 刘延柱.振动力学[M].北京:高等教育出版社,2019.
[64] MATHEWS J H, FINK K K. Numerical Methods Using Matlab [M]. 2005.
[65] 张雄,王天舒.计算动力学[M].北京:清华大学出版社,2007.
[66] 徐斌,高跃飞,余龙.Matlab有限元结构动力写分析与工程应用[M].北京:清华大学出版社,2009.
[67] 曾攀.有限元分析及应用[M].北京:清华大学出版社,2004.
[68] 宋文尧,张牙.卡尔曼滤波[M].科学出版社,1991.
[69] CHEN C T. Linear system theory and design[M]. Holt, Rinehart, and Winston, 1984.
[70] MAES K, LOURENS E, NIMMEN K V, et al. Design of sensor networks forinstantaneous inversion of modally reduced order models in structural dynamics[J].Mechanical Systems and Signal Processing, 2015.
[71] CUMBO R, MAZZANTI L, TAMAROZZI T, et al. Advanced optimal sensorplacement for Kalman-based multiple-input estimation[J]. Mechanical Systems and Signal Processing, 2021, 160(3):107830.
[72] 郭忠. 矩阵正定性的判定及线性方程组AX=b 的反问题求解[J]. 科学通报,1987(02):95-95.
[73] JONKMAN J M, BUTTERFIELD S, MUSIAL W, et al. Definition of a 5MWReference Wind Turbine for Offshore System Development[J]. office of scientific &technical information technical reports, 2009.
[74] JONKMAN J, MUSIAL W. Subtask 2 The Offshore Code Comparison Collaboration(OC3) IEA Wind Task 23 Offshore Wind Technology and Deployment[J]. Technical Report, 2010.
[75] DONG X, LIAN J, Wang H. Vibration source identification of offshore wind turbine structure based on optimized spectral kurtosis and ensemble empirical mode decomposition[J]. Ocean Engineering, 2019, 172(JAN.15):199 -212.
[76] HAECKELL M W, ROLFES R. Monitoring a 5 MW offshore wind energy converter—Condition parameters and triangulation based extraction of modal parameters[J].Mechanical Systems & Signal Processing, 2013, 40(1):322-343.

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专题工学院_海洋科学与工程系
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谢远春. 基于Kalman 滤波海上风机结构健康监测 研究[D]. 深圳. 南方科技大学,2022.
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