Comparison of Data-Driven Modeling Approaches for Control Optimization of Floating Offshore Wind Turbines

TL;DR

This paper compares various low-fidelity modeling approaches, including a novel LPV-based DFSM, for optimizing floating offshore wind turbine control, demonstrating significant speed-ups with maintained accuracy.

Contribution

Introduces a novel LPV-based derivative function surrogate model for wind turbine optimization, outperforming classical and deep learning models in efficiency and accuracy.

Findings

DFSM achieves nearly 50x speed-up over high-fidelity models.

DFSM balances accuracy and computational efficiency better than other models.

Proposed approach enhances control optimization for offshore wind turbines.

Abstract

Models that balance accuracy against computational costs are advantageous when designing wind turbines with optimization studies, as several hundred predictive function evaluations might be necessary to identify the optimal solution. We explore different approaches to construct low-fidelity models that can be used to approximate dynamic quantities and be used as surrogates for design optimization studies and other use cases. In particular, low-fidelity modeling approaches using classical systems identification and deep learning approaches are considered against derivative function surrogate models ({DFSMs}), or approximate models of the state derivative function. This work proposes a novel method that utilizes a linear parameter varying (LPV) modeling scheme to construct the DFSM. We compare the trade-offs between these different models and explore the efficacy of the proposed DFSM approach in approximating wind turbine performance and design optimization studies for controllers. Results show that the proposed DFSM approach balances computational time and model accuracy better than the system identification and deep learning based models. Additionally, the DFSM provides nearly a fifty times speed-up compared to the high-fidelity model, while balancing accuracy.