Position Estimation of a Wave Energy Converter Using Limited Sensor Data

Abstract Advanced control systems for wave energy converters (WECs), such as the model predictive control or reinforcement learning, require precise real-time estimation of the buoy’s position (surge, sway, and heave) and orientation (roll, pitch, and yaw). For WECs operating below the water surface, Global Navigation Satellite System data is unavailable for accurate position tracking, necessitating reliance on limited sensor measurements. One such WEC is the CETO system, which features a fully submerged buoy tethered to the seafloor by three mooring lines. The system is equipped with an inertial measurement unit (IMU) to track the buoy’s spatial orientation, along with three encoders that measure the rotation of the shaft in the rotary power take-off, which can be correlated with the extension of each mooring line. However, in a full-scale setup, the IMU may not accurately measure the yaw angle due to magnetic interference from the power take-off electrical equipment. This paper presents an algorithm for estimating the position of the CETO device in the open ocean using limited sensor data. The methodology, adapted from cable robotics, is validated through numerical simulations and tank testing. Additionally, the paper offers recommendations on how incorporating additional sensors and measurements can improve the accuracy of state estimation for the WEC.