WIND TURBINE FAULT DETECTION METHOD BASED ON DYNAMIC NEIGHBORHOOD INDEX RECONSTRUCTION

Qian Xiaoyi; Sun Tianhe; Jang Xingyu; Wang Baoshi

doi:10.19912/j.0254-0096.tynxb.2023-0855

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (9) : 557-563. DOI: 10.19912/j.0254-0096.tynxb.2023-0855

WIND TURBINE FAULT DETECTION METHOD BASED ON DYNAMIC NEIGHBORHOOD INDEX RECONSTRUCTION

Qian Xiaoyi, Sun Tianhe, Jang Xingyu, Wang Baoshi

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Abstract

The changeable working condition is one of the main causes of false and missing alarms in wind turbine fault detection. For this purpose, a fault detection method based on dynamic coordination of neighborhood scale and threshold is proposed. The reconstruction indexes of stepped neighbor and state separation degree are defined to evaluate the ability of fault separation, on this basis, the iterative correction strategy of dynamic neighborhood scale is constructed. A fusion method of static component and dynamic component for threshold is proposed to suppress the false positives and missing positives caused by the abrupt change of working condition through dynamic neighborhood size and dynamic threshold. Ten common faults of megawatt wind turbines are used in the experiments and verified the proposed method can more effectively separate the abnormal state from the normal state and reduce the false alarm rate and missing false rate.

Key words

wind turbines / fault detection / reconstruction / complex working conditions / dynamic neighborhood size / dynamic threshold

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Qian Xiaoyi, Sun Tianhe, Jang Xingyu, Wang Baoshi. WIND TURBINE FAULT DETECTION METHOD BASED ON DYNAMIC NEIGHBORHOOD INDEX RECONSTRUCTION[J]. Acta Energiae Solaris Sinica. 2024, 45(9): 557-563 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0855

References

[1] STETCO A, DINMOHAMMADI F, ZHAO X Y, et al.Machine learning methods for wind turbine condition monitoring: a review[J]. Renewable energy, 2019, 133: 620-635.
[2] 李强, 张宇献. 基于量子进化在线序贯极限学习机的变桨系统故障检测[J]. 太阳能学报, 2022, 43(1): 44-51.
LI Q, ZHANG Y X.Fault detection based on online sequential extreme learning machine using quantum evolutionary algorithm for pitch system[J]. Acta energiae solaris sinica, 2022, 43(1): 44-51.
[3] 胡姚刚, 刘怀盛, 时萍萍, 等. 风电机组偏航系统故障诊断与寿命预测综述[J]. 中国电机工程学报, 2022, 42(13): 4871-4884.
HU Y G, LIU H S, SHI P P, et al.Overview of fault diagnosis and life prediction for wind turbine yaw system[J]. Proceedings of the CSEE, 2022, 42(13): 4871-4884.
[4] KORKOS P, LINJAMA M, KLEEMOLA J, et al.Data annotation and feature extraction in fault detection in a wind turbine hydraulic pitch system[J]. Renewable energy, 2022, 185: 692-703.
[5] HU C Z, YANG Q, HUANG M Y, et al.Sparse component analysis based under-determined blind source separation for bearing fault feature extraction in wind turbine gearbox[J]. IET renewable power generation, 2017, 11(3): 330-337.
[6] WANG Y F, MA X D, QIAN P.Wind turbine fault detection and identification through PCA-based optimal variable selection[J]. IEEE transactions on sustainable energy, 2018, 9(4): 1627-1635.
[7] PAN H N, QIN M, ZHANG J, et al. Bayesian networks in electric reliability assessment of doubly-fed wind turbine generator[J]. Applied mechanics and materials, 2014, 494/495: 1791-1794.
[8] 郑小霞, 李美娜, 王靖, 等. 基于PSO优化核主元分析的海上风电机组运行工况分类[J]. 电力系统保护与控制, 2016, 44(16): 28-35.
ZHENG X X, LI M N, WANG J, et al.Operational conditions classification of offshore wind turbines based on kernel principal analysis optimized by PSO[J]. Power system protection and control, 2016, 44(16): 28-35.
[9] 刘永前, 王飞, 时文刚, 等. 基于支持向量机的风电机组运行工况分类方法[J]. 太阳能学报, 2010, 31(9): 1191-1197.
LIU Y Q, WANG F, SHI W G, et al.Operation condition classification method for wind turbine based on support vector machine[J]. Acta energiae solaris sinica, 2010, 31(9): 1191-1197.
[10] 董玉亮, 李亚琼, 曹海斌, 等. 基于运行工况辨识的风电机组健康状态实时评价方法[J]. 中国电机工程学报, 2013, 33(11): 88-95.
DONG Y L, LI Y Q, CAO H B, et al.Real-time health condition evaluation on wind turbines based on operational condition recognition[J]. Proceedings of the CSEE, 2013, 33(11): 88-95.
[11] HE Q P, WANG J.Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes[J]. IEEE transactions on semiconductor manufacturing, 2007, 20(4): 345-354.
[12] HE Q P, WANG J.Large-scale semiconductor process fault detection using a fast pattern recognition-based method[J]. IEEE transactions on semiconductor manufacturing, 2010, 23(2): 194-200.
[13] ZHOU Z, WEN C L, YANG C J.Fault detection using random projections and k-nearest neighbor rule for semiconductor manufacturing processes[J]. IEEE transactions on semiconductor manufacturing, 2015, 28(1): 70-79.
[14] COLHERINHAS G B, DE MORAIS M V G, MACHADO M R. Spectral model of offshore wind turbines and vibration control by pendulum tuned mass dampers[J]. International journal of structural stability and dynamics, 2022, 22(5): 22-30.
[15] ZHANG Y X, WANG K F, QIAN X Y, et al.Robust fault-detection based on residual K-L divergence for wind turbines[J]. IET renewable power generation, 2019, 13(13): 2400-2408.
[16] PASHAZADEH V, SALMASI F R, ARAABI B N.Data driven sensor and actuator fault detection and isolation in wind turbine using classifier fusion[J]. Renewable energy, 2018, 116: 99-106.
[17] RUIZ M, MUJICA L E, ALFÉREZ S, et al. Wind turbine fault detection and classification by means of image texture analysis[J]. Mechanical systems and signal processing, 2018, 107: 149-167.
[18] JONKMAN J, BUTTERFIELD S, MUSIAL W, et al.Definition of a 5-MW reference wind turbine for offshore system development[J]. Office of scientific and technical information technical reports, 2009, 2(1): 1-75
[19] ODGAARD P F, JOHNSON K E.Wind turbine fault detection and fault tolerant control-an enhanced benchmark challenge[C]//2013 American Control Conference. Washington, DC, USA, 2013: 4447-4452.
[20] SUN H, GUO Y Q, ZHAO W L, Fault detection for aircraft turbofan engine using a modified moving window KPCA[J]. IEEE access, 2020(8): 166541-166552.