Consistency Monitoring of Wind Turbine Blade Loads Based on MEMS Optical Fiber Sensing

Yong Xue; Yang Li; Xiangye Fan; Binshan Xie; Zhiyuan Ma; Lin Lin; Mengnan Cao

doi:10.4108/ew.9387

Authors

Yong Xue State Power Investment Group Xinjiang Energy and Chemical Emin Co., Ltd
Yang Li State Power Investment Group Xinjiang Energy and Chemical Emin Co., Ltd
Xiangye Fan State Power Investment Group Xinjiang Energy and Chemical Emin Co., Ltd
Binshan Xie State Power Investment Group Xinjiang Energy and Chemical Emin Co., Ltd
Zhiyuan Ma Shanghai Power Equipment Research Institute
Lin Lin Shanghai Power Equipment Research Institute
Mengnan Cao Shanghai Power Equipment Research Institute

DOI:

https://doi.org/10.4108/ew.9387

Keywords:

MEMS-FBG Sensors, Wind Turbine Blade Monitoring, Load Consistency Index, Strain-to-Load Calibration, SCADA Integration, Signal Drift Analysis , Anomaly Detection

Abstract

INTRODUCTION: This paper presents a comprehensive structural monitoring framework for wind turbine blades based on MEMS-FBG (micro-electro-mechanical systems—fiber Bragg grating) sensor fusion technology.

OBJECTIVES: The system integrates high-resolution strain and vibration sensing across multiple blade segments, combined with real-time data processing, fault detection, and SCADA-level visualization.

METHODS: A multilayered load consistency model is introduced, incorporating thermal compensation, strain-to-load calibration, and a novel consistency index (η) to quantify inter-blade aerodynamic symmetry.

RESULTS: Experimental validation was conducted on a 2.0 MW wind turbine over a 60-day continuous monitoring campaign. Static calibration demonstrated a load reconstruction accuracy within ±3%, while dynamic data revealed a strong correlation between load and blade vibration (R ≥ 0.86).

CONCLUSION: Fault simulation through pitch angle manipulation confirmed the system’s rapid alarm response within 3 seconds for major asymmetry events. Additionally, signal drift testing showed MEMS-FBG sensors exhibited 80–95% lower drift than conventional resistance strain gauges under rotating and EMI-intensive conditions.

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References

[1] Wang Y, Ma K, Peng Q, Wu Y. Design and performance study of real-time, reliable, and highly accurate carbon fiber reinforced polymer-fiber Bragg grating sensors for wind turbine blade strain monitoring. Opt Eng. 2024;63(3):037108-037108.

[2] Zhang L, Wei J. A machine vision method for identifying blade tip clearance in wind turbines. Sensors. 2024;24(18):5935.

[3] Mardanshahi A, Sreekumar A, Yang X, Barman SK, Chronopoulos D. Sensing techniques for structural health monitoring: A state-of-the-art review on performance criteria and new-generation technologies. Sensors. 2025;25(5):1424.

[4] Henderson R, Azhari F, Sinclair A. Natural frequency transmissibility for detection of cracks in horizontal axis wind turbine blades. Sensors. 2024;24(14):4456.

[5] Zhang K, Pakrashi V, Murphy J, Hao G. Inspection of floating offshore wind turbines using multi- rotor unmanned aerial vehicles: literature review and trends. Sensors. 2024;24(3):911.

[6] Firoozi AA, Hejazi F, Firoozi AA. Advancing wind energy efficiency: A systematic review of aerodynamic optimization in wind turbine blade design. Energies. 2024;17(12):2919.

[7] Gu R, Zhang S, Zhu J, Zhu H, Li Y. Damage- related imbalance identification for UAV composite propeller blades based on bidirectional temporal convolutional network and a flexible sensing system. Meas Sci Technol. 2024;35(11):116126.

[8] Bosmans J, Gallas S, Smeets V, Kirchner M, Geens L, Croes J, Desmet W. Experimental validation of virtual torque sensing for wind turbine gearboxes based on strain measurements. Wind Energy. 2025;28(2):e2955.

[9] Gutiérrez JM, Astroza R, Jaramillo F, Orchard M, Guarini M. Evolution of modal parameters of composite wind turbine blades under short-and long-term forced vibration tests. J Civil Struct Health Monit. 2024;14(4):1059-1074.

[10] Zhang C, Shan G, Roh B. Fair federated learning for multi-task 6G NWDAF network anomaly detection. IEEE Trans Intell Transp Syst. 2024.

[11] Grandhi SH. Integrating HMI display module into passive IoT optical fibre sensor network for water level monitoring and feature extraction. World J Adv Eng Technol Sci. 2021;2:132-139.

[12] Kundu P. Review of rotating machinery elements condition monitoring using acoustic emission signal. Expert Syst Appl. 2024;252:124169.

[13] Meng W, Bachilo SM, Weisman RB, Nagarajaiah S. A review: Non-contact and full-field strain mapping methods for experimental mechanics and structural health monitoring. Sensors. 2024;24(20):6573.

[14] Ligęza P, Jamróz P, Socha K. Development trends of air flow velocity measurement methods and devices in renewable energy. Energies. 2025;18(2):412.

[15] Firoozi AA, Hejazi F, Firoozi AA. Advancing wind energy efficiency: A systematic review of aerodynamic optimization in wind turbine blade design. Energies. 2024;17(12):2919.

[16] Kumar PM, Kamruzzaman MM, Alfurhood BS, Hossain B, Nagarajan H, Sitaraman SR. Balanced performance merit on wind and solar energy contact with clean environment enrichment. IEEE J Electron Devices Soc. 2024;12:808-823.

[17] Adwant AV, Singh M, Deshmukh S, Singh VK. Development of portable dynamic torque power measurement system. Int J Adv Mechatron Syst. 2024;11(2):95-101.

[18] Qin L, Zhang L, Feng J, Zhang F, Han Q, Qin Z, Chu F. A hybrid triboelectric-piezoelectric smart squirrel cage with self-sensing and self-powering capabilities. Nano Energy. 2024;124:109506.

[19] Wu Z, et al. Enhanced on-site testing of DC current transformers using improved EMD filtering and high-precision synchronization. Mari Pap Corrugado. 2025;8-15. doi:10.71442/mari2025-0002.

[20] Chen B, Jia Y, Narita F, Kurita H, Shi Y. Screen- printed piezoelectric composites for vibrational energy harvesting in combination with structural composite laminates for powering a sensing node. Compos Part B Eng. 2024;273:111274.

[21] Xie Y, Zhang S, Meng X, Nguyen DT, Ye G, Li H. An innovative sensor integrated with GNSS and accelerometer for bridge health monitoring. Remote Sens. 2024;16(4):607.

[22] Fayyad TM, Taylor S, Feng K, Hui FKP. A scientometric analysis of drone-based structural health monitoring and new technologies. Adv Struct Eng. 2025;28(1):122-144.

[23] de Mooij C, Martinez M. A critical comparison of shape sensing algorithms: The calibration matrix method versus iFEM. Sensors. 2024;24(11):3562.

[24] Guan X, Yao Y, Wang K, Liu Y, Pan Z, Wang Z, et al. Wireless online rotation monitoring system for UAV motors based on a soft-contact triboelectric nanogenerator. ACS Appl Mater Interfaces. 2024;16(35):46516-46526.

[25] Simon M, Din SM. Performance evaluation of self-organizing features in wireless sensor networks. TK Techforum J. 2025;1:12-19