Research | Gdańsk University of Technology

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Research in wind energy is crucial for advancing the efficiency, sustainability, and integration of wind power into the energy mix. Offshore Wind Energy Center of Gdansk Tech (CMEW) adopts three main priority research lines:

1. Digitalization and machine learning (ML) techniques in wind energy

Turbine Performance Optimization: ML algorithms can analyze real-time operational data from wind turbines to identify patterns, detect anomalies, and optimize performance. By monitoring factors such as turbine vibrations, power output, and environmental conditions, ML can help identify potential maintenance issues before they lead to downtime, thus improving turbine reliability and reducing operational costs.

Predictive Maintenance: Digitalization combined with ML enables predictive maintenance of wind turbines by analyzing historical and real-time data to forecast equipment failures and schedule maintenance proactively. This approach helps minimize downtime, extend equipment lifespan, and reduce maintenance costs.

2. Operations and maintenance (O&M) of wind turbines is crucial for enhancing the efficiency, reliability, and cost-effectiveness of wind energy production. Key areas of CMEW research in this field:

Condition Monitoring and Predictive Maintenance: Developing advanced sensors and monitoring systems to continuously assess the health of wind turbine components such as blades, gearboxes, and generators. Predictive maintenance techniques use data analytics and machine learning algorithms to anticipate component failures and schedule maintenance proactively, minimizing downtime and reducing O&M costs.

Remote Monitoring and Diagnostics: Advancements in remote monitoring technologies enable real-time monitoring and diagnostics of wind turbine performance from a central control center. Research in this area focuses on improving data analytics algorithms and communication technologies to enable early fault detection and troubleshooting, especially for offshore wind farms where access is challenging.

Blade Inspection and Repair: Developing innovative inspection techniques, such as drones equipped with cameras and sensors, for assessing the condition of wind turbine blades. Research also focuses on automated repair methods for addressing blade damage, including composite patching and additive manufacturing techniques.

Cold Climate Operations: In regions with cold climates, research is focused on understanding the impact of low temperatures, ice formation, and extreme weather conditions on wind turbine performance and reliability. This includes studying de-icing systems, cold weather lubrication, and structural integrity under harsh environmental conditions.

End-of-Life Decommissioning: Research addresses the challenges associated with the decommissioning and recycling of wind turbines at the end of their operational lifespan. This includes developing strategies for turbine dismantling, recycling of materials such as fiberglass and metals, and environmentally responsible disposal of components.

3. Floating wind support structures are innovative platforms designed to support wind turbines in offshore environments where water depths are too great for fixed-bottom foundations. These structures enable the deployment of wind turbines in deep waters, opening up vast new areas for offshore wind energy development.

Operations & Maintenance Strategies: Developing effective maintenance strategies, including access methods for offshore inspection and repair, to ensure the long-term reliability and performance of floating wind farms.

Mooring and Dynamic Response: Understanding the dynamic behavior of floating structures and optimizing mooring systems to ensure stability, minimize motion, and withstand extreme weather conditions is crucial.

Cost Reduction: One of the primary challenges for floating wind is reducing costs to make it competitive with other forms of energy generation. Research focuses on optimizing design, materials, manufacturing processes, and installation techniques to lower overall project costs.