Abstract
Scour is one of the most serious challenges affecting the normal operation of offshore wind power. Whether scour protection measures are effective directly affects the safety and economical efficiency of an offshore wind farm. Data mining techniques and analytical tools based on monitoring data analysis plays a critical role in making appropriate strategic decisions. Post-protection scour inspection was continuously carried out on a typical offshore wind farm composed of 72 turbines installed with monopile foundations for three times over the course of two years. The geospatial properties of scour, including maximum scour depth, maximum scour extension, and scouring and siltation volume, were analyzed in detail. The possible autocorrelation of scour data measured for each turbine was identified using data-driven decision-making process. The results indicate that the characteristics of the investigated scour and their spatial dependence on the turbines of the inspected offshore wind farm generally show an upward trend over time. A High-High area in the local geospatial dependency map of maximum scour depth was identified, and an equation was drawn for predicting development of maximum scour depth. These findings show indications to reduce the operation and maintenance costs of offshore wind farms and improve the efficiency for upgrading scour protection on specific offshore wind foundations.
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