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

Abstract. This paper investigates fault detection in offshore wind permanent-magnet synchronous generators (PMSGs) for demagnetization and eccentricity faults (both static and dynamic) at various severity levels. The study utilizes a high-speed PMSG model on the National Renewable Energy Laboratory (NREL) 5 MW reference offshore wind turbine 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 supervisory control and data acquisition (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 (air gap 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 the outside of the stator housing, demonstrate superior performance in fault detection and sensitivity to fault severity levels. In particular, radial components of stray flux can successfully distinguish between eccentricity and demagnetization.