Optimising offshore wind efficiency with condition monitoring systems

The costs associated with operation and maintenance (O&M) for offshore wind turbines account for about 25 to 30 per cent of the total operational cost. These costs are so high due in part to the difficulty of accessing offshore turbines, which are located far out at sea where weather conditions can vary greatly.

Regular and well-targeted maintenance is essential for an offshore wind farm to run optimally in terms of both cost efficiency and energy production. Detecting faults at an early stage ultimately reduces maintenance costs, so more and more operators are introducing condition monitoring systems (CMS).

A CMS enables operators to monitor turbine health and performance remotely. The CMS continuously tracks the status and health of each turbine within a wind farm to ensure optimal performance and early fault detection. The CMS helps to detect issues before they cause further damage, allowing for timely repairs.

The CMS continuously monitors and recognises changes in the performance and behaviour of critical components such as bearings, gearboxes and generators. By analysing specific parameters and comparing them to those recorded under “normal” operating conditions, the CMS can detect potential faults at an early stage.

All CMS follow a six-step process to identify, diagnose and manage faults in wind turbines, converting raw physical data into actionable recommendations for O&M. Each step plays a critical role.

Data acquisition is the initial phase and involves receiving and converting physical measurements into digital data for further analysis. The data acquisition process converts physical elements such as vibration, oil debris, temperature and pressure into analogue signals.

Analogue signals are continuous signals that represent physical quantities. Unlike discrete digital signals, analogue signals can have infinite values within a range, making them ideal for capturing real-world elements. The signals are then converted into a digital format, making it possible to process, store and analyse the data.

The data processing phase converts raw digitised measurements into meaningful indicators. It involves filtering and analysing data to identify patterns or anomalies that suggest potential issues, providing clear insights for maintenance decisions.

The detection phase evaluates collected data to identify whether the wind turbine’s condition is normal or abnormal.

The diagnosis phase confirms whether a detected abnormality is a fault or a false alarm. It analyses detailed data to pinpoint the exact location of the issue and assesses the severity of the problem.

The prognosis phase estimates the remaining useful life of a component based on identified faults and current operating conditions. It utilises historical data and patterns from previous failures to predict how similar issues have progressed.

The recommendation phase generates actionable insights based on the diagnosed faults. After validating a fault and assessing its severity, the CMS finally suggests specific maintenance actions.

A digital twin is a virtual representation of a physical asset enabled through data and simulators for real-time prediction, monitoring, control and optimisation of the asset in order to improve decision making throughout the asset’s life cycle.

For offshore wind, this means creating virtual replicas of physical wind farms. By integrating operational data from the CMS, digital twins can simulate various scenarios, helping to identify potential faults and optimise maintenance schedules. The technology allows for shared real-time data analysis between the digital and physical systems. This enables operators to forecast the lifespan of turbines, improve monitoring and perform predictive maintenance.

There are a number of challenges related to wind turbine monitoring with a digital twin. First, there is a high initial cost when implementing the software and hardware, which must be updated regularly with the latest technology for optimal performance. Implementing digital twin technology also involves a steep learning curve, so operators must invest significant time and resources in training and skill development. Finally, there are cybersecurity risks due to the vast amount of data being shared.

Despite these challenges, the numerous long-term benefits offered by a digital twin can significantly improve project success and sustainability.