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Research on Fault Diagnosis of Converters and Sensors in Doubly-Fed Wind Power Generation Systems

Author(s)

Siying Lyu

Affiliation(s)

Guangzhou College of Commerce, Guangzhou, Guangdong, China

Abstract

Due to harsh environmental conditions like high temperatures, humidity, vibrations, and electromagnetic interference, the power electronic converters and sensors of doubly-fed wind turbines are susceptible to damage and malfunction. This may lead to the failure of other components, cause wind turbines to shut down, reduce wind power generation, and affect the stable operation of the power grid. This paper explores fault diagnosis algorithm integration for doubly-fed wind power systems under grid voltage balance and imbalance conditions, considering system model uncertainties in scenarios involving multiple complex faults in converters and sensors. By combining model-based observers with data-driven deep learning techniques, a hybrid model-and-data-driven fault diagnosis method is proposed to enhance the safety and reliability of doubly-fed wind power generation systems. The study proposes a proportional resonant observer and a hybrid model-data driven algorithm, which are applied to fault diagnosis of converter and sensor systems in both balanced and unbalanced grid conditions. The research is of great practical significance for improving the safety and reliability of doubly-fed wind power generation systems and reducing the operation and maintenance costs. It also provides new ideas for research on wind power system condition monitoring, robust fault observer design, and complex system fault diagnosis.

Keywords

Doubly-Fed Wind Power System; Power Converter; Sensor Failure; Fault Diagnosis

References

[1] Qiao W, Lu D. A survey on wind turbine condition monitoring and fault diagnosis—part I: components and subsystems. IEEE Transactions on Industrial Electronics, 2015, 62(10):6536–6545. [2] Ribrant J, Bertling L. Survey of failures in wind power systems with focus on Swedish wind power plants during 1997-2005. IEEE Transactions on Energy Conversion, 2007, 22(1):167–173. [3] Campos-Delgado D U, Espinoza-Trejo D R. An observer-based diagnosis scheme for single and simultaneous open-switch faults in induction motor drives. IEEE Transactions on Industrial Electronics, 2011, 58(2): 671-679. [4] Espinoza-Trejo D R, Campos-Delgado D U, Bossio G, et al. Fault diagnosis scheme for open-circuit faults in field-oriented control induction motor drives. IET Power Electronics, 2013, 6(5): 869-877. [5] An Q T, Sun L, Sun L Z. Current residual vector-based open-switch fault diagnosis of inverters in PMSM drive systems. IEEE Transactions on Power Electronics, 2015, 30(5): 2814-2827. [6] Mohamed-Amine Y, Michel K, Johan G. Augmented-state-observer-based diagnostics of open-circuit and sensor faults in DFIG wind turbines. IEEE Transactions on Power Electronics, 2023, 38(12): 16085-16099. [7] Poon J, Jain P, Konstantakopoulos I C, et al. Model-based fault detection and identification for switching power converters. IEEE Transactions on Power Electronics, 2017, 32(2): 1419-1430. [8] Jung S M, Park J S, Kim H W, et al. An MRAS-based diagnosis of open-circuit fault in PWM voltage-source inverters for PM synchronous motor drive systems. IEEE Transactions on Power Electronics, 2013, 28(5): 2514-2526. [9] Jlassi I, Estima J O, El Khil S K, et al. Multiple open-circuit faults diagnosis in back-to-back converters of PMSG drives for wind turbine systems. IEEE Transactions on Power Electronics, 2015, 30(5): 2689-2702. [10] Gan C, Wu J, Yang S, et al. Fault diagnosis scheme for open-circuit faults in switched reluctance motor drives using fast Fourier transform algorithm with bus current detection. IET Power Electronics, 2016, 9(1): 20-30. [11] Cheng F, Qu L, Qiao W, et al. Fault diagnosis of wind turbine gearboxes based on DFIG stator current envelope analysis. IEEE Transactions on Sustainable Energy, 2019, 10(3): 1044-1053. [12] Fu Y, Ren Z, Wei S, et al. Using flux linkage difference vector in early inter-turn short circuit detection for the windings of offshore wind DFIGs. IEEE Transactions on Energy Conversion, 2021, 36(4): 3007-3015. [13] Zhao S, Chen Y, Liang F, et al. Identifying early stator fault severity in DFIGs based on adaptive feature mode decomposition and multiscale complex component current trajectories. IEEE Transactions on Instrumentation and Measurement. 2024, 73: 3527916. [14] Hamatwi E, Barendse P, Khan A. A case for micromachines in laboratory-based DFIG wind turbine systems for fault studies. IEEE Transactions on Industry Applications, 2023, 59(2):1754 - 1764. [15] Mousavi Y, Bevan G, Kucukdemiral I, et al. Observer-based high-order sliding mode control of DFIG-based wind energy conversion systems subjected to sensor faults. IEEE Transactions on Industry Applications, 2024, 60(1): 1750-1759. [16] Chakraborty S, Chakrabarti A, Maiti D, et al. A rotor-tied DFIG scheme with one phase open circuit fault-tolerant capability. IEEE Transactions on Industrial Electronics, 2025, 72(5): 4343-4352.