Vessel intrusion interception utilising unmanned surface vehicles for offshore wind farm asset protection

Abstract

Remotely located offshore wind farms (OWFs) are usually in unmanned operation, and are severely at risk from accidental or deliberate man-made damages, such as cable faults caused by vessel anchoring. In this work, a proactive alarming and interception method utilising unmanned surface vehicles (USV) is proposed to prevent suspect vessels from intruding OWF areas and damaging the assets. More specifically, the vessel intrusion interception system keeps monitoring the motion of surrounding vessels and assessing their potential intrusive risks. When a suspect intruder is identified, USV will be automatically deployed to proactively intercept the target vessel with an alarming and expelling system. In particular, a risk assessment model based on the analytic hierarchy process (AHP) and entropy weight method (EWM) is established to predict the potential intrusive risk, and an improved fast marching square (FMS) algorithm is designed for USV path planning to guarantee dynamic and reliable vessel interception. In order to verify the effectiveness of the proposed method, case studies have been conducted for the Zhejiang Jiaxing 2 OWF with authentic vessel traffic data. Results indicate that the proposed risk assessment model based on AHP-EMW is able to correctly predict the potential intrusion risk in various scenarios, and the improved FMS path planning algorithm with multi-objective optimisation is capable of providing both efficient and safe trajectories for USV interception.