In the context of the energy conservation and emissions reduction goals, as well as the energy security strategy, wind power generation is playing an increasingly important role in the global energy system. Wind turbine (WT) is the core equipment for converting wind energy into electric energy, and it is the key link to ensure the stable operation of wind power systems, reduce maintenance costs, and improve power generation efficiency. Therefore, it is important to improve the reliability of WTs and reduce the economic losses caused by faults. Condition monitoring and fault diagnosis technologies for WTs, especially using intrinsic electrical signals that does not rely on additional sensors, is becoming a research hotspot for developing a more cost-effective monitoring method. In this thesis, an encoder-free order tracking based electrical signature analysis method (OT-ESA) is proposed for online fault monitoring and diagnosis of WTs with permanent magnet synchronous generators (PMSG). In this method, the instantaneous frequency is estimated by using the zero-crossing point of the current signal to realize the angular domain resampling of the variable speed non-stationary signal, and then the fast Fourier transform (FFT) spectrum, envelope spectrum and time synchronization average spectrum analysis are carried out through the resampled signal, and the characteristic frequencies such as blade unbalance, gear tooth defect and bearing fault are extracted. The experimental platform is based on a semi-direct-drive fan, combined with a rectifier bridge, sliding resistance and a variety of voltage/current sensors to carry out a variety of working conditions testing. The results show that this method can efficiently and accurately identify typical wind power faults without additional sensors, and has the potential for embedded implementation and engineering deployment, providing a low-cost, fast-response and high-reliability solution for wind power system condition monitoring.
| Date of Award | 24 Sept 2025 |
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| Original language | English |
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| Supervisor | Fengshou Gu (Main Supervisor) & Helen Miao (Co-Supervisor) |
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