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Electrical Engineering
Erschienen in:

04.06.2024 | Original Paper

Deep learning-based fuzzy decision support system-based fault diagnosis of wind turbine generators in electrical machines

verfasst von: Wei Pang, Kangming Xu, Qingyuan Wu, Chenyue Wang, Jingyue Li, Nan Yin

Erschienen in: Electrical Engineering | Ausgabe 1/2025

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Abstract

Precise monitoring and diagnosis outwit the faults in generator rotors, preventing malfunction and resurrections. This article, therefore, introduces a hybrid decision support system for wind turbine fault diagnosis. The hybrid system assimilates deep learning functions and fuzzy optimization linearly to prevent faults. First, the deep learning paradigm inputs the rotor speed and vibration for the recurrent identification of abnormal frequency harmonics. The identified frequency harmonics are verified with the output power in the conventional operation routine to compute the fault intensity. This harmonic change varies the training intensity to prevent major faults. Secondly, fuzzy optimization inputs the difference in energy generated between different scenarios and identifies the optimal (maximum) performance outcome. Such identification improves turbine operation and maintenance decisions, mitigating the rotor faults. The decision intervals are variable, abiding by the optimization of learning routines required for smooth rotor operations of the turbine.
Vorheriger Artikel A novel hybrid approach for efficient energy management in battery and supercapacitor based hybrid energy storage systems for electric vehicles
Nächster Artikel Economic operation of residential load using IOT-based renewable energy management system

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Literatur
1.
Sheng X, Wan S, Han X, He Y, Wang X (2022) Impact of actual wind speed distribution on the fault characteristic of DFIG rotor winding asymmetry. IEEE Trans Instrum Meas 71:1–14MATH
2.
Freeman B, Tang Y, Huang Y, VanZwieten J (2022) Physics-informed turbulence intensity infusion: a new hybrid approach for marine current turbine rotor blade fault detection. Ocean Eng 254:111299CrossRefMATH
3.
Fang J, Hu W, Liu Z, Chen W, Tan J, Jiang Z, Verma AS (2022) Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning. Renew Sustain Energy Rev 168:112788CrossRef
4.
Dameshghi A, Refan MH (2021) Combination of condition monitoring and prognosis systems based on current measurement and PSO-LS-SVM method for wind turbine DFIGs with rotor electrical asymmetry. Energy Syst 12:203–232CrossRef
5.
Glowacz A (2023) Thermographic fault diagnosis of electrical faults of commutator and induction motors. Eng Appl Artif Intell 121:105962CrossRefMATH
6.
Touti W, Salah M, Bacha K, Chaari A (2022) Condition monitoring of a wind turbine drivetrain based on generator stator current processing. ISA Trans 128:650–664CrossRef
7.
Guo Z, Pu Z, Du W, Wang H, Li C (2022) Improved adversarial learning for fault feature generation of wind turbine gearbox. Renew Energy 185:255–266CrossRefMATH
8.
Dong W, Zhang S, Hu M, Zhang L, Liu H (2022) Intelligent fault diagnosis of wind turbine gearboxes based on refined generalized multi-scale state joint entropy and robust spectral feature selection. Nonlinear Dyn 107(3):2485–2517CrossRefMATH
9.
Xu S, Tao S, Zheng W, Chai Y, Ma M, Ding L (2021) Multiple open-circuit fault diagnosis for back-to-back converter of PMSG wind generation system based on instantaneous amplitude estimation. IEEE Trans Instrum Meas 70:1–13
10.
Ding X, Gong Y, Wang C, Zheng Z (2024) Artificial intelligence based abnormal detection system and method for wind power equipment. Int J Thermofluids 21:100569CrossRefMATH
11.
Ding X, Zhang Y, Ye Z (2021) Current sensors offset fault online estimation in permanent magnet synchronous generator (PMSG) drives for offshore wind turbines. IEEE Access 9:135996–136003CrossRef
12.
Touti W, Salah M, Sheng S, Bacha K (2024) An envelope time synchronous averaging for wind turbine gearbox fault diagnosis. J Vib Eng Tech 12:6513–6525
13.
Cho S, Choi M, Gao Z, Moan T (2021) Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks. Renew Energy 169:1–13CrossRefMATH
14.
Wen X, Xu Z (2021) Wind turbine fault diagnosis based on ReliefF-PCA and DNN. Expert Syst Appl 178:115016CrossRefMATH
15.
Tian M, Su X, Chen C, Luo Y, Sun X (2023) Bearing fault diagnosis of wind turbines based on dynamic multi-adversarial adaptive network. J Mech Sci Tech 37:1637–1651CrossRefMATH
16.
Vives J (2022) Vibration analysis for fault detection in wind turbines using machine learning techniques. Adv Comput Intell 2(1):15CrossRefMATH
17.
Lin KC, Hsu JY, Wang HW, Chen MY (2024) Early fault prediction for wind turbines based on deep learning. Sustain Energy Technol Assess 64:103684
18.
Al-Hiealy MRJ, Shikh MSBAM, Jalil AB, Rahman SA, Jarrah M (2021) Management switching angles real-time prediction by artificial neural network. Indones J Electr Eng Comput Sci 23(1):110–119
19.
Rouabah B, Toubakh H, Kafi MR, Sayed-Mouchaweh M (2022) Adaptive data-driven fault-tolerant control strategy for optimal power extraction in presence of broken rotor bars in wind turbine. ISA Trans 130:92–103CrossRef
20.
Xing Z, Chen M, Cui J, Chen Z, Xu J (2022) Detection of magnitude and position of rotor aerodynamic imbalance of wind turbines using convolutional neural network. Renew Energy 197:1020–1033CrossRefMATH
21.
Hübner GR, Pinheiro H, de Souza CE, Franchi CM, da Rosa LD, Dias JP (2021) Detection of mass imbalance in the rotor of wind turbines using support vector machine. Renew Energy 170:49–59CrossRef
22.
Yin L, Chen L, Liu D, Huang X, Gao F (2021) Quantum deep reinforcement learning for rotor side converter control of double-fed induction generator-based wind turbines. Eng Appl Artif Intell 106:104451CrossRef
23.
Tan Y, Zhang H, Zhou Y (2019) Fault detection method for permanent magnet synchronous generator wind energy converters using correlation features among three-phase currents. J Mod Power Syst Clean Energy 8(1):168–178CrossRefMATH
24.
Yang Q, Liu G, Bao Y, Chen Q (2021) Fault detection of wind turbine generator bearing using attention-based neural networks and voting-based strategy. IEEE/ASME Trans Mechatron 27(5):3008–3018CrossRefMATH
25.
Zhang L, Zhang H, Cai G (2022) The multiclass fault diagnosis of wind turbine bearing based on multisource signal fusion and deep learning generative model. IEEE Trans Instrum Meas 71:1–12MATH
26.
Chen J, Yao W, Lu Q, Ren Y, Duan W, Kan J, Jiang L (2022) Adaptive active fault-tolerant MPPT control of variable-speed wind turbine considering generator actuator failure. Int J Electr Power Energy Syst 143:108443CrossRef
27.
Su Y, Meng L, Kong X, Xu T, Lan X, Li Y (2022) Small sample fault diagnosis method for wind turbine gearbox based on optimized generative adversarial networks. Eng Fail Anal 140:106573CrossRef
28.
Mostafa MA, El-Hay EA, Elkholy MM (2023) Optimal low voltage ride through of wind turbine doubly fed induction generator based on bonobo optimization algorithm. Sci Rep 13(1):7778CrossRefMATH
29.
Jiang G, Fan W, Li W, Wang L, He Q, Xie P, Li X (2022) DeepFedWT: a federated deep learning framework for fault detection of wind turbines. Measurement 199:111529CrossRefMATH
30.
31.
Kandukuri ST, Senanyaka JSL, Robbersmyr KG (2019) A two-stage fault detection and classification scheme for electrical pitch drives in offshore wind farms using support vector machine. IEEE Trans Ind Appl 55(5):5109–5118CrossRef
Metadaten
Titel
Deep learning-based fuzzy decision support system-based fault diagnosis of wind turbine generators in electrical machines
verfasst von
Wei Pang
Kangming Xu
Qingyuan Wu
Chenyue Wang
Jingyue Li
Nan Yin
Publikationsdatum
04.06.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
Electrical Engineering / Ausgabe 1/2025
Print ISSN: 0948-7921
Elektronische ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-024-02426-4

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