AI powers next generation of offshore wind technologies

The study reveals a rapidly evolving field where AI plays a pivotal role in accelerating design, enabling predictive maintenance, optimizing control systems, and powering intelligent digital replicas of wind turbines, all essential to making offshore wind farms more efficient, safer, and cost-effective.

A new study published in Energies analyses how artificial intelligence (AI) is transforming the design, operation, and long-term reliability of floating offshore wind turbines (FOWTs) - a sector critical to achieving global clean energy goals.

Titled “Artificial Intelligence in Floating Offshore Wind Turbines: A Critical Review of Applications in Design, Monitoring, Control, and Digital Twins”, the paper systematically reviews 115 peer-reviewed studies to map how machine learning, neural networks, reinforcement learning, and digital twin systems are being applied to the world’s most complex renewable energy systems. The authors also identify the limitations and research gaps that could hinder AI’s practical deployment in next-generation wind farms.

The study reveals a rapidly evolving field where AI plays a pivotal role in accelerating design, enabling predictive maintenance, optimizing control systems, and powering intelligent digital replicas of wind turbines, all essential to making offshore wind farms more efficient, safer, and cost-effective.

Revolutionizing design and optimization in floating wind systems

The study answers a key question: How can AI-driven modeling reshape the design and optimization of floating offshore wind turbines?

The authors begin by noting that FOWTs face a dual challenge — mechanical instability in deep-sea environments and the computational cost of simulating coupled aero-hydro-servo-elastic dynamics. Traditional simulation approaches, while accurate, demand significant processing time, limiting their usefulness for real-time optimization. AI, particularly through surrogate modeling and machine learning regression techniques, offers a faster alternative.

The study highlights how artificial neural networks (ANNs) and Gaussian process regression models are increasingly being used to approximate complex simulation outputs, enabling engineers to conduct rapid multi-objective optimization. These models can predict structural stress, platform motion, and aerodynamic performance within seconds, allowing iterative design testing that would otherwise take days.

AI has reduced computational costs and improved design adaptability across various FOWT platforms, including spar-buoy, semi-submersible, and tension-leg systems. However, they also caution that the credibility of AI-generated surrogates depends heavily on their uncertainty quantification, a factor still underdeveloped in much of the current research.

By integrating physics-informed neural networks (PINNs), researchers have started bridging this gap, ensuring that AI predictions adhere to physical constraints. This hybrid approach blends data-driven insights with the underlying physics of fluid and structural dynamics, enhancing both speed and reliability.

As a result, AI-assisted design is paving the way toward automated, data-driven engineering workflows that can adapt turbine structures to changing wind and wave conditions while minimizing material costs and fatigue damage over time.

Smart monitoring and control: From fault detection to adaptive learning

The review explores how AI is revolutionizing structural health monitoring (SHM) and control systems in offshore environments. These applications aim to ensure turbine stability, reduce maintenance costs, and extend operational lifespan, critical objectives as offshore wind farms move into deeper waters and harsher climates.

The study finds that conventional SHM techniques rely on threshold-based signal analysis, which often fails to distinguish between normal variations and actual faults. In contrast, modern AI models use deep learning architectures such as autoencoders and convolutional neural networks (CNNs) to automatically identify damage patterns in vibration, strain, or acoustic data.

These models learn from operational data to detect subtle anomalies in real time, providing early warnings of component degradation. The review notes that such systems are already capable of reducing false alarms and improving diagnostic accuracy under varying sea states.

Beyond monitoring, AI is increasingly applied to active control. Algorithms based on reinforcement learning (RL) and model-predictive control (MPC) have been tested for optimizing pitch and torque adjustments, minimizing platform motions, and balancing power production against structural fatigue. RL-based controllers, for instance, learn optimal strategies through trial-and-error simulation, outperforming traditional PID controllers in dynamic offshore conditions.

The authors underline that adaptive control systems are particularly promising for floating wind–wave hybrid platforms, where coupled dynamics introduce extreme variability. By incorporating AI into these systems, turbines can automatically respond to environmental changes, maintaining stability and maximizing energy output.

One emerging frontier is safe reinforcement learning, which integrates stability constraints into training processes, ensuring that learned behaviors do not compromise turbine integrity during high-wind or extreme-sea scenarios. The review suggests this could become a cornerstone for autonomous offshore wind operations.

Digital twins and the path to autonomous offshore wind farms

The study’s third major theme examines how AI-powered digital twins (DTs) are reshaping predictive maintenance and lifecycle management of floating turbines.

Digital twins, virtual replicas of physical assets, allow continuous synchronization between real-world operations and simulated performance models. When embedded with machine learning and online learning capabilities, these twins become self-updating systems that can predict failures, optimize maintenance schedules, and assess remaining useful life (RUL).

The review identifies several AI-driven architectures that combine reduced-order physics models with neural network calibration, enabling real-time updates as new data streams in from sensors. These systems can simulate turbine responses under changing conditions, forecast degradation trends, and provide actionable insights for operators and maintenance crews.

AI-enhanced digital twins also support probabilistic risk assessment, integrating uncertainty quantification into predictions to improve reliability and decision-making. This feature is crucial for offshore operations where access for repairs is limited and downtime costs are significant.

However, the authors caution that most digital twin implementations remain in simulation or laboratory stages. Field validation using full-scale turbine data is still rare, and interoperability between AI tools and existing supervisory control and data acquisition (SCADA) systems remains a key challenge. The authors stress that establishing open standards and shared data frameworks will be essential for scaling these technologies across the global offshore wind industry.

As AI-enabled twins evolve, the paper envisions a future of autonomous offshore wind farms, where fleets of floating turbines self-monitor, self-optimize, and self-repair through coordinated digital intelligence.

Gaps, limitations, and future research directions

Despite clear progress, the study highlights persistent gaps that must be addressed before AI can fully transform floating offshore wind.

Among the 115 studies reviewed, most rely on simulation-based validation rather than real-world field data. This limitation undermines the credibility of results and delays industry adoption. The authors also note a lack of standardized evaluation metrics, making it difficult to compare models or assess generalizability.

Another challenge is data scarcity. Offshore environments are data-poor due to limited sensor coverage and harsh weather conditions. Without access to large, diverse datasets, AI models risk overfitting to narrow scenarios. The authors call for international collaborations to build open-access benchmark datasets that can accelerate reliable model development.

The review further calls for transparency and interpretability in AI decision-making. Black-box models may produce accurate predictions but are difficult to trust in safety-critical applications. Hybrid methods that integrate explainable AI (XAI) with physics-based constraints are therefore recommended to ensure accountability.

Lastly, the paper calls for uncertainty quantification (UQ) across all AI applications. Whether in design optimization, control, or digital twins, understanding model confidence is essential for certification and operational risk management. Without standardized approaches to quantify uncertainty, regulatory bodies may hesitate to approve AI-driven systems for offshore deployment.

Future work should prioritize the creation of shared digital infrastructures where AI models, data, and simulation environments interact seamlessly across stakeholders. Partnerships between academia, industry, and government agencies will be critical to translate laboratory breakthroughs into real-world solutions.