Dynamic Modelling of Multiple Faults in Tidal Stream Turbines

Tidal stream turbines (TSTs) operate in complex seabed environment, rendering them highly susceptible to various faults, including impact faults, biofouling, and demagnetization. Additionally, the high energy density of the seabed, combined with the TSTs' small rotational inertia, increases their sensitivity to variations in tidal velocity. To reduce the impact of tidal velocity variations on fault detection and improve detection precision, a fault model incorporating multiple faults based on the TST's dynamic characteristics has been developed. The impact of tidal velocity is minimized through system decoupling and linearization. Simulation experiments demonstrate that additive faults in the TST can be effectively detected using the constructed model.

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Publication Details

Published
2025-03-21
DOI
https://doi.org/10.1109/sgai64825.2025.11009448
Primary Topic
Wind Energy Research and Development
Type
article
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article

Dynamic Modelling of Multiple Faults in Tidal Stream Turbines

Tianzhen Wang, Chunwei Jin
Wind Energy Research and Development
article

Dynamic Modelling of Multiple Faults in Tidal Stream Turbines

Tianzhen Wang, Chunwei Jin
article en

Abstract

Tidal stream turbines (TSTs) operate in complex seabed environment, rendering them highly susceptible to various faults, including impact faults, biofouling, and demagnetization. Additionally, the high energy density of the seabed, combined with the TSTs' small rotational inertia, increases their sensitivity to variations in tidal velocity. To reduce the impact of tidal velocity variations on fault detection and improve detection precision, a fault model incorporating multiple faults based on the TST's dynamic characteristics has been developed. The impact of tidal velocity is minimized through system decoupling and linearization. Simulation experiments demonstrate that additive faults in the TST can be effectively detected using the constructed model.

Shanghai Maritime University (CN)
Affordable and clean energy
Openalex Percentile: Top 84%
Wind Energy Research and Development
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