Integrated wind-wave control of semi-submersible floating offshore wind turbine platforms (FOWT-Control)

Offshore wind energy is becoming a major electricity provider with future expansion in deep water. Floating platforms can access water depths typically greater than 30 m, but have the disadvantage of platform motions due to combined waves and time varying thrust from turbine motion. Platform stabilisation is critically important for improving performance, reducing downtime and enabling safe access. Lost electrical output alone for a proposed 15 MW machine can be £20k per day at today's prices. Moreover, misalignment of the turbine axis with wind direction due to yaw and pitch causes power loss and undesirable blade stresses. In addition to pitch and surge in the wave direction, roll and yaw cross wave may occur due to multi-directional wave fields. Thus this project has two distinct aims both impacting on through life cost: Aim 1: to optimally minimise platform motion during power production by integrated (holistic) preview control of wave and wind effects on platform and turbines. A key reliability goal is to ensure acceleration at the nacelle due to pitch and surge is less than the recommended 0.2-0.3g, and to minimise damaging electrical surges and fatigue of structural components. Aim 2: to absolutely minimise platform motion for safe maintenance during personnel and material transfers by boat or helicopter and minimise debilitating motion effects on personnel during maintenance work. The illustrative case employed is the popular semi-sub floater concept which has comparatively shallow draft and simple deployment. Platform stabilisation will be achieved by combining: (i) pumped tank control between semi-sub columns to minimise pitch and roll as employed in ships, (ii) blade pitch control, already used in wind turbine control and (iii) yaw control for alignment with the wind direction. This multi-objective non-causal control problem requires future knowledge of both wave and wind forcing functions to achieve optimality.