Rigorous incremental testing and validation are essential to advancing wave energy converter (WEC) technology. Although laboratory wave tank testing remains common, it poses challenges in scaling hydrodynamic responses and power take-off (PTO) dynamics. These issues are more pronounced for WECs with tethered heave plates due to complex interactions between the structure, tether, heave plate, and PTO; all of which often exceed tank depth and scaling limits. Field testing enables full-system evaluation but introduces practical limitations, including environmental variability, limited sensing, and measurement uncertainty. A knowledge gap remains in how to overcome these limitations to extract meaningful insights and validate WEC numerical models using field test data. Moreover, full-scale PTOs exhibit significant nonlinearities, such as generator inertia, internal losses, and inefficiencies across the full energy conversion chain, that are not captured in current PTO models. This highlights the need for improved modeling techniques to realistically estimate useful power and energy output. This study uses a field-deployed WEC with a tethered heave plate to demonstrate how combining statistical and spectral analyses enables comprehensive insight and validation of WEC models using field data. It also advances PTO modeling by incorporating generator inertia and fitting a parametric relationship between shaft speed and useful power based on PTO dynamometer test data. This approach predicted power and energy within 9% of field measurements, whereas conventional models overestimated these output by up to a factor of 3. The improved PTO modeling yields more realistic levelized cost of energy (LCOE) estimates to better guide future full-scale WEC development.

This work uses sparse arrays of ocean wave buoys to create a linear reconstruction of the sea surface and provide deterministic wave predictions at a future time and nearby position (i.e., within a few wavelengths). The predictions are constrained to be within the statistics of recently observed waves. This recently established method is applied in post-processing at two distinct projects related to 1) a wave energy converter and 2) an offshore wind platform. The conditions range from scale-model tank testing to an operational open-ocean wind farm. Relative to a conventional statistical forecasting with random waves, the method achieves at least 60% improvement in prescribing the next several waves arriving at a given target location. Work is ongoing to implement this method in realtime, using radio modems to transmit raw motion data (5 Hz sampling) from the buoy array to a central node that continually updates a 30-second prediction window with less than 1-second latency. The deterministic wave predictions can be used to improve control strategies for platforms at sea, with improvements in efficiency and reductions in dynamic loads.

As wave energy conversion technology advances, recharge of autonomous underwater vehicles has emerged as a promising application for this at-sea power. We bring together an interdisciplinary team to create a simulation framework linking hydrodynamic modeling, autonomous docking and navigation algorithms, and a power tracking model to better understand how a full wave energy converter–autonomous underwater vehicle system could be modeled. A floating point absorber wave energy converter is modeled and analyzed under various wave conditions. We incorporate three different dock designs, using the modeled dock motion and simulated wave-induced currents to test our autonomous docking algorithm. We couple the output of this algorithm to the hydrodynamic model to simulate autonomous docking. This shows that docking with a floating third body is successful in most sea states, while a dock rigidly mounted to the wave energy converter presents difficulty for autonomous docking. Finally, we incorporate a power model to better understand the feasibility and capabilities of a wave energy converter–underwater vehicle system in simulated wave environments. This shows that this system is comfortably supported in the majority of sea states, and provides an estimate of the on-board power storage required to maximize vehicle mission time.