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Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS

Author

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  • Quan Zhou

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Taotao Xiong

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Mubin Wang

    (State Grid Lishui Electric Power Supply Company, Lishui 323000, China)

  • Chenmeng Xiang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Qingpeng Xu

    (State Grid Chengdu Power Supply Company, Chengdu 610041, China)

Abstract

The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT) faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA) systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS) is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the traditional threshold method.

Suggested Citation

  • Quan Zhou & Taotao Xiong & Mubin Wang & Chenmeng Xiang & Qingpeng Xu, 2017. "Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS," Energies, MDPI, vol. 10(7), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:898-:d:103313
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    References listed on IDEAS

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    1. Lapira, Edzel & Brisset, Dustin & Davari Ardakani, Hossein & Siegel, David & Lee, Jay, 2012. "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, Elsevier, vol. 45(C), pages 86-95.
    2. Mengnan Cao & Yingning Qiu & Yanhui Feng & Hao Wang & Dan Li, 2016. "Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data," Energies, MDPI, vol. 9(10), pages 1-18, October.
    3. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    4. Otilia Elena Dragomir & Florin Dragomir & Veronica Stefan & Eugenia Minca, 2015. "Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources," Energies, MDPI, vol. 8(11), pages 1-15, November.
    5. Mohamed Abdelrahem & Ralph Kennel, 2016. "Fault-Ride through Strategy for Permanent-Magnet Synchronous Generators in Variable-Speed Wind Turbines," Energies, MDPI, vol. 9(12), pages 1-15, December.
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    Cited by:

    1. Fei Peng & Yanmei Wang & Haiyang Xuan & Tien V. T. Nguyen, 2022. "Efficient road traffic anti-collision warning system based on fuzzy nonlinear programming," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 456-461, March.
    2. Wenbin Su & Hongbo Wei & Penghua Guo & Ruizhe Guo, 2021. "Remote Monitoring and Fault Diagnosis of Ocean Current Energy Hydraulic Transmission and Control Power Generation System," Energies, MDPI, vol. 14(13), pages 1-18, July.
    3. Zemali, Zakaria & Cherroun, Lakhmissi & Hadroug, Nadji & Hafaifa, Ahmed & Iratni, Abdelhamid & Alshammari, Obaid S. & Colak, Ilhami, 2023. "Robust intelligent fault diagnosis strategy using Kalman observers and neuro-fuzzy systems for a wind turbine benchmark," Renewable Energy, Elsevier, vol. 205(C), pages 873-898.
    4. Xiaoyi Qian & Yuxian Zhang & Mohammed Gendeel, 2019. "State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines," Energies, MDPI, vol. 12(11), pages 1-18, May.
    5. Ivan Marović & Ivana Sušanj & Nevenka Ožanić, 2017. "Development of ANN Model for Wind Speed Prediction as a Support for Early Warning System," Complexity, Hindawi, vol. 2017, pages 1-10, December.
    6. 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.

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