Accurate Analysis of Wind Turbine Aerodynamic Power Sensitivity to Yaw and Pitch Errors Using a Second-Order Response Surface Method with a Microsensor for Error Reduction

Document Type : Original Article

Authors

1 Advanced Micro and Nano devices Lab., Department of MEMS and NEMS, School of Intelligent Systems, University of Tehran, Tehran, Iran

2 Associate Professor at Department of MEMS and NEMS, School of Intelligent Systems, University of Tehran, Tehran, Iran

Abstract

In this study, the effects of two frequent operational errors in wind turbines yaw misalignment and blade pitch offset were quantified and modeled for the turbine aerodynamic response in two operating regions: Region 2 (below-rated) and Region 3 (rated/above-rated). To obtain results that are generalizable and comparable across different scales, three reference turbines (3.4 MW, 5 MW, and 10 MW) were selected and time-domain simulations were performed using OpenFAST/AeroDyn (BEM) under steady wind conditions. Quasi-steady quantities, including the power coefficient and aerodynamic power (and, in intermediate steps, aerodynamic torque), were extracted from the steady-state window. To systematically cover the parameter space , a design of experiments (DOE) approach was applied, and quasi-steady quadratic response-surface surrogate models (RSM/QS) were fitted separately for Regions 2 and 3. Validation was carried out by direct comparison between surrogate predictions and OpenFAST results (including parity plots). In addition to 2D sensitivity curves with respect to yaw and pitch, response maps and 3D surfaces were provided to visualize continuous variations of and across the operating space. Finally, using the final quadratic equations and their coefficients as low-cost relations, the impact of different sensor uncertainties on turbine power output was evaluated via a Monte Carlo method, showing that LiDAR and MEMS sensors yield the smallest power losses. Specifically, for the MEMS sensor, the worst-case (P95) power loss is approximately –  for yaw-only misalignment across all three turbines, and approximately –  for the combined “wind-speed error + induced pitch miscommand” scenario.

Keywords

Main Subjects


  • Tony Burton, David Sharpe, Nick Jenkins and Ervin Bossanyi. WIND ENERGY HANDBOOK Chichester • New York • Weinheim • Brisbane • Singapore • Toronto: JOHN WILEY & SONS, LTD; 2001.
  • Mike T. van Dijk, Jan-Willem van Wingerden, Turaj Ashuri, Yaoyu Li and Mario A. Rotea. Yaw-Misalignment and its Impact on Wind Turbine Loads and Wind Farm Power Output. In The Science of Making Torque from Wind; 2016: IOP Publishing.
  • Andreas Rott, Leo Höning, Paul Hulsman, Laura J. Lukassen, Christof Moldenhauer, and Martin Kühn. Wind vane correction during yaw misalignment for horizontal-axis wind turbines. Wind Energy. 2023;: 1755–1770.
  • Ravi Pandit, David Infield, and Tim Dodwel. Operational Variables for Improving Industrial Wind Turbine Yaw Misalignment Early Fault Detection Capabilities Using Data-Driven Techniques. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT. 2021.
  • Jaime Liew, Albert M. Urbán, and Søren Juhl Andersen. Analytical model for the power–yaw sensitivity of wind turbines operating in full wake. Wind Energy. 427–437;: 2020.
  • Rafiei, K. & Koohsorkhi, J. (2025). Investigation and Simulation of Micro-Electromechanical Sensors on Wind Turbine Performance. Journal of Sustainable Energy Systems, 4 (3), 233-249.
  • Bo Jing, Zheng Qian, Yan Pei, Lizhong Zhang, Tingyi Yang. Improving wind turbine efficiency through detection and calibration of yaw misalignment. Renewable Energy. 2020;: 1217-1227.
  • Knud A. Kragh, Morten H. Hansen and Lars C. Henriksen. Sensor comparison study for load alleviating wind turbine pitch control. WIND ENERGY. 2014;: 1891–1904.
  • S. Leu, J.M. Yu, J.J. Miau, S.J. Chen. MEMS flexible thermal flow sensors for measurement of unsteady flow above a pitching wind turbine blade. Experimental Thermal and Fluid Science. 2016;: 167–178.
  • Branlard, J. Jonkman, B. Lee, B. Jonkman, M. Singh, E. Mayda and K. Dixon. Improvements to the Blade Element Momentum Formulation of OpenFAST for Skewed Inflows. Journal of Physics: Conference Series. 2024; 2767(2).
  • E. P. Box, D. W. Behnken DWB. Some New Three Level Designs for the Study of Quantitative Variables. G. E. P. Box, D. W. Behnken. 1960; 4: 455-475.
  • Montgomery DC. Design and analysis of experiments: John Wiley & Sons, ; 2013.
  • Yasemin Ayaz Atalan, Mete Tayanç, Kamil Erkan and Abdulkadir Atalan MTKEaAA. Development of Nonlinear Optimization Models for Wind Power Plants Using Box-Behnken Design of Experiment: A Case Study for Turkey. Sustainability. 2020.
  • Ngwarai Shambira, Golden Makaka and Patrick Mukumba. Velocity Augmentation Model for an Empty Concentrator-Diffuser-Augmented Wind Turbine and Optimisation of Geometrical Parameters Using Surface Response Methodology. Sustainability. 2024.
  • Tomasz Lipecki, Paulina Jami´nska-Gadomska and Andrzej Sumorek. Influence of Ultrasonic Wind Sensor Position on Measurement Accuracy under Full-Scale Conditions. sensors. 2020.
  • Mingjia Shangguan, Jiawei Qiu, Jinlong Yuan, Zhifeng Shu, Lingfeng Zhou and Haiyun Xia. Doppler Wind Lidar From UV to NIR: A Review With Case Study Examples. Frontiers in Remote Sensing. 2022.
  • Fatemeh Ejeian, Shohreh Azadi, Amir Razmjou, Yasin Orooji, Ajay Kottapalli, Majid Ebrahimi Warkiani and et al. Design and applications of MEMS flow sensors: A review. ensors and Actuators A: Physical. 2019;: 483-502.
  • Moein Sarbandi and Hamid Khaloozadeh. Quantifying the impact of sensor precision on power output of a wind turbine: A sensitivity analysis via Monte Carlo simulation study. Wind Engineering. 2024;: 497–517.
  • Nicolas Schärer, Denis Mikhaylov, Cédric Sievi, Badoui Hanna, Caroline Braud, Julien Deparday, et al. Aerodynamic Performance and Impact Analysis of a MEMS-Based Non-Invasive Monitoring System for Wind Turbine Blades. arXiv. 2024.
  • Jonkman, S. Butterfield, W. Musial, and G. Scott. Definition of a 5-MW Reference Wind Turbine for Offshore System Development. Technical Report. Golden, Colorado:; 2009.
  • Pietro Bortolotti, Helena Canet Tarr´es, Katherine Dykes, Karl Merz, Latha Sethuraman, David Verelst, et al. IEA Wind Task 37 on Systems Engineering in Wind Energy IEA Wind Task 37 on Systems Engineering in Wind Energy. Technical Report. ; 2019.
  • Ganander H. The Use of a Code-generating System for the Derivation of the Equations for Wind Turbine Dynamics. WIND ENERGY. 2003;: 333–345.
  • PERDANA A. Dynamic Models of Wind Turbines: CHALMERS UNIVERSITY OF TECHNOLOGY; 2008.
  • Min-Soo Jeong, Sang-Woo Kim, In Lee, Seung-Jae Yoo and K.C. Park. The impact of yaw error on aeroelastic characteristics of a horizontal axis wind turbine blade. Renewable Energy. 2013;: 256-268.
  • Astolfi D. A Study of the Impact of Pitch Misalignment on Wind Turbine Performance. machines. 2019.
  • Pedro A. Galvani, Fei Sun and Kamran Turkoglu. Aerodynamic Modeling of NREL 5-MW Wind Turbine for Nonlinear Control System Design: A Case Study Based on Real-Time Nonlinear Receding Horizon Control. aerospace. 2016.
  • Tian-tian Zhang, Mohamed Elsakka, Wei Huang, Zhen-guo Wang, Derek B. Ingham, Lin Ma and et Winglet design for vertical axis wind turbines based on a design of experiment and CFD approach. Energy Conversion and Management. 2019;: 712-726.
  • Possolo A. Simple Guide for Evaluating and Expressing the Uncertainty of NIST Measurement Results. Technical. National Institute of Standards and Technology, Statistical Engineering Division Information Technology Laboratory; 2015.
  • Liu B, Zhao S, Yu X, Zhang L, Wang Q. A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model. Energies. 2020; 13(18)