March 25 2019 0Comment

Improve O&M performance of offshore wind farms with monitoring and data analytics

Maintenance is a key activity to keep offshore wind farms productive and profitable. As one paper explains (link), O&M makes up one third of the total cost of energy. It is not uncommon for offshore wind farms to have more than 60 turbines; the new farm Hornsea even has 174 turbines. Maintenance of all these turbines is a daily task, heavily influenced by weather conditions. Commonly fast-sailing CTVs are used to bring technicians from onshore to the turbines. There are two large risks involved with this process: seasickness and a transfer with a boat landing.

CTVs are relatively small and sail quickly, compared to the larger walk-to-work vessels (SOVs). Seasickness is therefore a more common issue onboard of CTVs. Seasickness can significantly impact the performance level of someone’s work. Research performed by TNO (link) shows that for starting symptoms of seasickness 20% of tasks start failing, ranging up to 60% for someone that suffers heavily. If the crew is so sick, it is likely more economical to stay in port.

There are no off-the-shelf solutions yet on the market for planning of logistics. Some research projects have been conducted, such as the O&M JIP (link). MO4 has developed a novel system to get a better understanding of the wellness level of the onboard crew. There are two main steps:

  1. Monitoring: the incoming weather and CTV motions and speed are measured with our MRU and logged with the BlackBox. Further, the comfort level of the onboard crew is monitored with a survey. Before leaving the vessel, personnel is asked a few basic questions to find out how they are feeling.
  2. Data analytics: based on the gathered data (measurements and survey) models are fitted. The hydrodynamics of a CTV are tricky, therefore we make use of machine learning algorithms to develop an accurate model. We can then correlate motions and other relevant aspects to the comfort levels of personnel.

A similar approach can be adopted for the transfer of personnel with a boat landing. These operations are very difficult to simulate, hence a data-driven approach is more useful.

Using monitoring and advanced data-analytics it is possible to gain more insight in comfort of personnel, and even make comfort level forecasts for future trips. With this knowledge it is possible to account for the wellness of technicians in the daily planning. The decision to go or to stay in port can be made with a much more sound basis. Further, optimization of the planning becomes possible by varying the route and sailing speed to avoid seasickness, and investigating which turbines can be safely visited. A great potential to save costs by ensuring technicians arrive safely and fit for their task at their place of work!

Besides this approach for CTVs, MO4 has also developed a module for SOVs. The goal of our systems is to provide the vessel operators with more insight in the motion characteristics of their vessels, leading to less unnecessary conservatism and consequently a higher workability.