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Fault diagnosis in direct drive permanent magnet generators for off-shore wind turbines

Sergakis Alexandros

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Year 2024
Type of Item Diploma Work
Bibliographic Citation Alexandros Sergakis, "Fault diagnosis in direct drive permanent magnet generators for off-shore wind turbines", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2024
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In the field of renewable energy, Offshore wind energy is important for Greece’s carbon neutrality by 2050.A study by ELIAMEP highlights the socio-economic value of offshore wind farms. The Cost Benefit Analysis (CBA) framework considers global and local impacts, focusing on reducing CO2 emissions and the need for compensation to local communities affected by offshore windturbines. These turbines,often employing direct drive permanent magnet generators (PMSGs),play a crucial role in harnessing wind power efficiently. The reliability and performance of these generators are important for uninterrupted energy production.This thesis studies fault diagnosis in direct drive permanent magnet generators, with a particular focus on C-GEN generators, aiming to enhance the maintenance and operation of offshore wind turbines. Our research explores various fault detection techniques in case of demagnetization (rotor fault), including Park’s Vector Approach (PVA), Extended Park’s Vector Approach (EPVA), Motor Current Signature Analysis (MCSA) and flux monitoring through the voltage of the sensors. These advanced diagnostic methods contribute to the overall efficiency and lifespan of offshore wind turbines, ensuring the sustainability of energy production.Various demagnetization percentages for 1 or 2 (non-adjacent at 22.5 ̋ apart) magnets are tested with different ohmic loads, which are simulated via SimcenterMAGNET for each of the steady-state cases of the C-gen generator. The voltage, current and torque measurements from the simulation are then sampled (at 6kHz) and post-processed using MATLAB to diagnose each case of demagnetization.After analyzing the signal processing output, we can detect the differences by observing the signals of healthy and faulty cases in time and frequency domain, specifically looking at harmonic frequencies.

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