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A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

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  • Pinjia Zhang

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Delong Lu

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

Abstract

Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed.

Suggested Citation

  • Pinjia Zhang & Delong Lu, 2019. "A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines," Energies, MDPI, vol. 12(14), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2801-:d:250256
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    References listed on IDEAS

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    1. Chen, Jinglong & Pan, Jun & Li, Zipeng & Zi, Yanyang & Chen, Xuefeng, 2016. "Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals," Renewable Energy, Elsevier, vol. 89(C), pages 80-92.
    2. Jenny Niebsch & Ronny Ramlau & Thien T. Nguyen, 2010. "Mass and Aerodynamic Imbalance Estimates of Wind Turbines," Energies, MDPI, vol. 3(4), pages 1-15, April.
    3. Lee, Jae-Kyung & Park, Joon-Young & Oh, Ki-Yong & Ju, Seung-Hwan & Lee, Jun-Shin, 2015. "Transformation algorithm of wind turbine blade moment signals for blade condition monitoring," Renewable Energy, Elsevier, vol. 79(C), pages 209-218.
    4. Soua, Slim & Van Lieshout, Paul & Perera, Asanka & Gan, Tat-Hean & Bridge, Bryan, 2013. "Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring," Renewable Energy, Elsevier, vol. 51(C), pages 175-181.
    5. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Eiriksson, Egill Thor, 2016. "Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes," Renewable Energy, Elsevier, vol. 91(C), pages 90-106.
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    Citations

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    Cited by:

    1. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    2. Alessandro Murgia & Robbert Verbeke & Elena Tsiporkova & Ludovico Terzi & Davide Astolfi, 2023. "Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis," Energies, MDPI, vol. 16(2), pages 1-20, January.
    3. Weldon Carlos Elias Teixeira & Miguel Ángel Sanz-Bobi & Roberto Célio Limão de Oliveira, 2022. "Applying Intelligent Multi-Agents to Reduce False Alarms in Wind Turbine Monitoring Systems," Energies, MDPI, vol. 15(19), pages 1-28, October.
    4. Yolanda Vidal, 2023. "Artificial Intelligence for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 16(4), pages 1-4, February.
    5. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    6. Jordi Burriel-Valencia & Ruben Puche-Panadero & Javier Martinez-Roman & Angel Sapena-Baño & Martin Riera-Guasp & Manuel Pineda-Sánchez, 2019. "Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines," Energies, MDPI, vol. 12(17), pages 1-18, August.
    7. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    8. Junshuai Yan & Yongqian Liu & Xiaoying Ren, 2023. "An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm," Energies, MDPI, vol. 16(10), pages 1-23, May.

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