21 Mar 2024
 | 21 Mar 2024
Status: this preprint is currently under review for the journal WES.

Unsupervised anomaly detection of permanent magnet offshore wind generators through electrical and electromagnetic measurements

Ali Dibaj, Mostafa Valavi, and Amir R. Nejad

Abstract. This paper investigates fault detection in offshore wind permanent magnet synchronous generators (PMSG) for demagnetization and eccentricity faults (both static and dynamic) at various severity levels. The study utilizes a high-speed PMSG model, on the NREL 5-MW reference offshore wind turbine, and at the rated wind speed, to simulate healthy and faulty conditions. An unsupervised convolutional autoencoder (CAE) model, trained on simulated signals from the generator in its healthy state, serves for anomaly detection. The main aim of the paper is to evaluate the possibility of fault detection by means of high-resolution electrical and electromagnetic signals, given that the typically low-resolution standard measurements used in SCADA systems of wind turbines often impede the early detection of incipient failures. Signals analyzed include three-phase currents, induced shaft voltage, electromagnetic torque, and magnetic flux (airgap and stray) from different directions and positions. The performance of CAE models is compared across time and frequency domains. Results show that in the time domain, stator three-phase currents effectively detect faults. In the frequency domain, stray flux measurements, positioned at the top, bottom, and sides of outside the stator housing, demonstrate superior performance in fault detection and sensitivity to fault severity levels. Particularly, radial components of stray flux can successfully distinguish between eccentricity and demagnetization.

Ali Dibaj, Mostafa Valavi, and Amir R. Nejad

Status: open (until 11 May 2024)

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Ali Dibaj, Mostafa Valavi, and Amir R. Nejad
Ali Dibaj, Mostafa Valavi, and Amir R. Nejad


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Short summary
This study emphasizes the need for effective condition monitoring in permanent magnet offshore wind generators to tackle issues like demagnetization and eccentricity. Utilizing a machine learning model and high-resolution measurements, we explore early fault detection methods. Our findings indicate that flux monitoring with affordable, easy-to-install stray flux sensors with frequency information offers a promising fault detection strategy for large MW offshore wind generators.