Balancing present & future

Wind turbine maintenance has become big business, and the industry is continuing to grow. According to energy business advisors Douglas-Westwood, offshore wind spending will average approximately €15 billion over the next 10 years, a significant proportion of which will be related to Operation and Maintenance (O&M).

However the vast size of the market and its continued growth does create challenges for owners, operators and managers alike. The fact that for large on and offshore wind farms over 70 per cent of expenditure comes from a site’s O&M means that being able to use and analyse performance data that helps to reduce costs and improve ROI is of vital importance.

Having the correct performance data allows managers to implement strategies that minimise the risk of wind turbine failure and resultant downtime. Data from a variety of different sources and vendors are managed and include varying turbine types, SCADA systems, condition monitoring systems (CMS) and software applications, which can then be turned into a set of useable actions.

A widely known way to collect information about the health of the wind turbine drivetrain is though analysis of existing data sources such as CMS, SCADA, inspections and maintenance records, which frequently contain unused data trapped away in siloed systems.

Access to this data provides O&M managers with is an entirely new set of data analytics, as well as significant cost savings and ROI, which presents the true value of the data received and will aid in the monitoring of faults across drivetrains.

Another consideration when addressing concerns surrounding O&M cost and wind turbine failure is determining an optimal maintenance strategy.

The main thing to understand about maintenance strategies is that they generally fit into one of three categories:

• Reactive or a run-to-failure strategy: This only takes place when a failure occurs. While it is easy to implement, it can prove highly costly and difficult to budget for and predict.
• Preventative maintenance or calendar-based maintenance: This is implemented when parts are actively replaced before failure to minimise repair costs, resulting in large costs incurred on replacing parts which could have potentially continued to run perfectly for years to come.
• Predictive or condition-based maintenance strategy: This lies in the middle of the previous two and focuses on predicting machinery condition and future component failures, ensuring maintenance can be performed before failures have the chance to manifest. Technologies are required to determine the condition     of a wind turbine and predict future failures.

Assessing the current condition is relatively simple, which is why tried and tested techniques such as vibration condition monitoring of the drivetrain and detailed inspections of the turbine are used to assess performance.

Problems can occur in assessing the life span of a gearbox. To enable predictive maintenance, wind turbine monitoring technology needs to deliver predictions of future component failures with at least 6-12 months lead time. To carry this out, a variety of technologies need to be used, such as vibration condition monitoring, oil monitoring, remaining useful life models, inspection & maintenance data and information on the turbine loading. Generally speaking these datasets are not analysed using a single predictive model, but only by analysing them together can you fully understand the current and future health of the machinery.

An example of this could be using vibration monitoring to predict main bearing failures. This can be very challenging but can deliver significant O&M cost savings. If an operator is able to plan the maintenance events with sufficient lead-time, they are often able to mobilise a crane or large vessel during the low wind season and have all the requisite parts and engineers ready, with also the possibility to carry out other additional maintenance at the same time while the crane or vessel is available.

The challenges that turbine failure can create are daunting, however there are technologies available that can aid O&M managers to develop and implement a predictive strategy, allowing them to assess and prevent failure and in turn remove barriers and deliver lower-cost wind turbine O&M.