The progress beyond the current state of the art lies in two aspects:
(1) The current theoretical and algorithmic research has difficulty in addressing all the challenges of the aforementioned effectiveness, robustness, integration and multi-scalability of control and monitoring systems for floating offshore wind turbines. In detail, all of the existing methods fail to develop an online system, because the computational complexity of these models is too high to allow for real-time dynamical simulations. Moreover, only a limited variety of methods are reported to model the fault diagnosis and optimal control system, especially for the floating offshore wind turbines currently in the literature, which might be attributed to the complicated coupling effects of the platform motions, generator speed, and blade pitch, all of which are highly dependent on the floating platform characteristics. The expected results derived from our project will fully address all the challenges of the effectiveness, robustness, integration and multi-scalability of the wind turbine control and fault diagnosis system for the floating offshore wind turbine. For instance, in this project, I develop a fully data-driven control algorithm called subspace predictive repetitive control for load control and fault accommodation. Then an adaptive control law is formulated to aim at the load reduction or fault accommodation. In addition, I build up a mixed model and signal-based fault diagnosis algorithm, which is able to take into account all the critical faults and failures in floating offshore wind turbines. All of these algorithms are demonstrated via high-fidelity simulations.
(2) Simulation and experimental research are available to evaluate the performance of the newly developed controller and monitoring system by establishing numerical simulations or experimental tests. However, existing literature on simulation and experimental research is still scarce. Most of the relevant work, to the best of the researcher’s knowledge, mainly considers the onshore wind turbine benchmark, with limited consideration of the FOWTs. In this project, the expected results will address the simulation and experimental research knowledge gap. First, we build up a new benchmark model for the floating offshore wind turbine, which opens up the inclusion of different kinds of critical faults and failures. Based on the benchmark model, the simulation research on the wind turbine control and fault diagnosis system can be implemented. Furthermore, we did hybrid software-in-the-loop wave basin experiments in MARIN in the Netherlands to validate the newly developed control system of the floating offshore wind turbine under several fault scenarios.
Based on the progress and the results derived from the EDOWE project, it is promising to apply the developed new control and condition monitoring techniques to the real floating offshore wind turbine in the near future. With an effective and optimal control and condition monitoring strategy developed in this project, the safety, reliability, robustness and cost-effectiveness of the wind turbine will be increased, which leads to an economical viable and more cost-effective floating wind energy exploitation, and thus a green future and zero-carbon emission. This is the socio-economic impact and the wider societal implications of this project.
