Description du poste

In this critical context, the proposed project main objective is the detection of tidal stream turbine biofouling than its estimation. The biofouling issue is expected to be addressed using electrical terminals (mainly the current) of the tidal turbine generator (doubly-fed induction or permanent magnet synchronous generators). Indeed, in addition to the increase to the above-cited impacts and the increase in the structural weight, the main effects of biofouling on a tidal turbine system should include: reduced power generation, reduced turbine durability, physical deformations in turbine blades, serious eccentricity of the turbine shaft, and bearing damages. These effects will obviously impact the turbine electric generator current [3-5]. It is therefore of paramount importance to achieve an early detection and estimation of biofouling so as to plan early stage removal before accumulation. To achieve the project main objective, a modeling step is expected. Indeed, based on the available biofouling literature [2], a first-order modeling of the biofouling should be carried out. This first-order model will therefore be used for the design of specific advanced signal processing techniques of the generator current for the detection of the biofouling. Afterwards, it is also expected to estimate the detected biofouling using either a failure (i.e. biofouling) severity index or designing a specific observer of the generator rotor angular acceleration to estimate the inertia, which is impacted by the biofouling. Finally, it is also expected to predict (prognosis) biofouling evolution using machine learning for maintenance scheduling purposes. This target will typically require using databases [6-7].

 

The project will be carried out at the Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 – http://irdl.fr/) in its PTR 4 (Pôle Thématique de Recherche) Systèmes Energétiques et Procédés at Brest (Université de Bretagne Occidentale –
https://www.univ-brest.fr/GB). The Ph.D. thesis project is financially supported by the PIA 3 Industries de la Mer Bretagne.

 

References
[1] M.E.H. Benbouzid, H. Titah-Benbouzid and Z. Zhou, Ocean Energy Technologies, Chap. 10097, Elsevier Encyclopedia of Sustainable Technologies, p. 2-13, ISBN 978-3-319-08421-1, 2017.
[2] H. Titah-Benbouzid and M.E.H. Benbouzid, “Biofouling issue on marine renewable energy converters: A state of the art review on impacts and prevention,” International Journal on Energy Conversion, vol. 5, n°3, pp. 67-78, May 2017.
[3] Z. Li, T. Wang, Y. Wang, Y. Amirat, M.E.H. Benbouzid and D. Diallo, “A wavelet threshold denoising-based imbalance fault detection method for marine current turbines,” IEEE Access, vol. 8, pp. 29815–29825, 2020.
[4] L. Saidi, M.E.H. Benbouzid, D. Diallo, Y. Amirat, E. Elbouchikhi and T. Wang, “Higher-order spectra analysis-based diagnosis method of blades biofouling in a PMSG driven tidal stream turbine,” Energies, vol. 13, n°11, 2888, pp. 1-18, June 2020.
[5] M. Zhang, T. Wang, T. Tang, M.E.H. Benbouzid and D. Diallo, “An imbalance fault detection method based on data normalization and EMD for marine current turbines,” ISA Transactions, vol. 68, pp. 302-312, May 2017.
[6] T. Berghout, M.E.H. Benbouzid and L.H. Mouss, “Leveraging labels information in a knowledge-driven approach for rolling-elements bearings remaining useful life prediction,” Energies, vol. 14, n°8, 2163, pp. 1–18, April 2021.
[7] T. Berghout, L.H. Mouss, T. Bentrcia, E. Elbouchikhi and M.E.H. Benbouzid, “A deep supervised learning approach for condition-based maintenance of naval propulsion systems,” Ocean Engineering, vol. 221, Article 108525, pp. 1–11, February 2021.

Modalités de candidature

The unique application document should include:

1) CV;

2) Cover letter including motivations and perspectives on the Ph.D. thesis project development;

3) Master or equivalent results transcripts.

Application to : Mohamed.Benbouzid@univ-brest.fr