Underwater target detection is the foundation and guarantee for the autonomous operation of underwater vehicles and is one of the key technologies in marine exploration. Due to the complex and special underwater environment, the detection effect is poor, and the detection precision is not high. In this paper, YOLOv5 (You Only Look Once v5) is used as the overall structural framework of the target detection algorithm, and improvement is made on the basis of its detection precision in the underwater environment. Specifically, an attention mechanism (Channel and Spatial Fusion Attention, CSFA) that fuses the channel attention and spatial attention is proposed and added to the YOLOv5 network framework, enabling the network to focus on both the prominent features of the detected object and the spatial information of the detected object. The proposed method was tested on the underwater target detection dataset provided by the China Underwater Robot Professional Competition. The experimental detection precision (P) reached 85%, the recall (R) reached 82.2%, and the mean average precision (mAP) reached 87.5%. The effectiveness of the proposed method was verified, and its underwater target detection performance was better than that of ordinary models.

To improve the efficiency and stability of an eccentric rotating wave energy converter (ERWEC), the adaptive hydraulic power-take-off (PTO) system with an accumulator is designed and developed. Experiments are performed to analyze the effects of trigger pressure, delay time, and open state duration on average output power and power fluctuation index. The results show that the effects of those three control parameters of accumulator on output power are strongly coupled. The experimental examples are designed based on the optimal Latin hypercube sampling (OLHS) method, and the nonparameterized agent models of control parameters to output power indices are established based on the Gaussian process regression (GPR) method. With the help of sensitivity analysis, it is found that the coupled effect of delay time and open state duration on the power fluctuation index is greater than that on the average output power. Furthermore, the optimal combination of control parameters is obtained by non-dominated sorting genetic algorithm-III (NSGA-III), which improves the amount and stability of output power from a hydraulic PTO system. This paper is of important significance for parameter setting of hydraulic PTO systems with an accumulator for wave energy converter, and provides the basis for the real-time adjustment of control parameters under complex sea conditions.

Hybrid systems that integrate wave energy converters (WECs) with floating offshore wind turbines (FOWTs) are considered to be key equipment to deeply exploit marine renewable energy. The power take-off (PTO) system is an important component of the hybrid system, whose parameters also have a significant impact on the hybrid system’s performance. In this paper, a wind-wave hybrid system using hydraulic PTO systems is proposed. A numerical simulation framework based on the linear wave theory and basic equations of hydraulic components is built and verified. The influence of six critical hydraulic parameters on the wave energy capture and motion response performance of the hybrid system is investigated. Specifically, the parameters of piston area, motor displacement, and equivalent generator damping affect the performance of the hybrid system similar to changing the damping term of the PTO system. The parameters of the initial gas volume and the pre-charged pressure of the accumulator affect the wave power capture only for short wave periods, while the motion response of the hybrid system increases with the increase of these two parameters. The parameter of orifice area of the throttle valve affects the performance of the hybrid system slightly only when it is small. The optimal value of partial hydraulic parameters and their corresponding peak performance are also discussed.

Reducing the carbon emissions of ships and increasing the utilization of marine renewable energy are the important ways to achieve the goal of carbon neutrality in ocean engineering. Establishing an accurate mathematical model is the foundation of simulating the motion of marine vehicles and structures, and it is the basis of operation energy efficiency optimization and prediction of power generation. System identification from observed input–output data is a practical and powerful method. However, for modeling objects with different characteristics and known information, a single modeling framework can hardly meet the requirements of model establishment. Moreover, there are some challenges in system identification, such as parameter drift and overfitting. In this work, three robust methods are proposed for generating ocean hydrodynamic models based on Bayesian regression. Two Bayesian techniques, semi-conjugate linear regression and noisy input Gaussian process regression are used for parametric and nonparametric gray-box modeling and black-box modeling. The experimental free-running tests of the KRISO very large crude oil carrier (KVLCC2) ship model and a multi-freedom wave energy converter (WEC) are used to validate the proposed Bayesian models. The results demonstrate that the proposed schemes for system identification of the ship and WEC have good generalization ability and robustness. Finally, the developed modeling methods are evaluated considering the aspects required conditions, operating characteristics, and prediction accuracy.

In this paper, a variable damper is proposed to regulate the efficiency of a two-body wave energy converter (WEC) with mechanically driven power take-off (PTO). The variable damper introduces logic constraints into the WEC system, which can be translated into a mixed logical dynamical form with the dynamics of real-valued variables, the dynamics of logic variables, and their interactions. A hybrid model predictive control (MPC) method is used to determine the control inputs, which has the capacity to handle various constraints. The performance is assessed through simulations to evaluate the effectiveness of the proposed method. The achievable performance improvements of the proposed hybrid MPC are shown by means of comparative analysis with uncontrolled WEC devices. The results show that the proposed hybrid MPC has a high requirement on the lower bound of the variable damper and the maximum damping is used only at low relative velocities to achieve the optimum phase, like latching control. The hybrid MPC performs exceptionally well under wave conditions with a small significant wave height and long wave period, improving the power generation of the uncontrolled system up to 22.5%. And, the prediction error has a significant effect on hybrid MPC performance, especially for long prediction horizon.

To obtain the optimal mooring mode and the best-matching wave condition of an eccentric rotor wave energy converter (ERWEC), a physical model of the ERWEC was developed. Ten mooring modes and eight wave conditions were set up. Several experiments were carried out to analyze the influence of mooring modes and wave conditions on the mooring and energy-harvesting performances of the ERWEC. The results showed that the mooring and energy-harvesting performances changed significantly for the same mooring mode under various regular wave conditions, but the opposite situation was found under irregular wave conditions. The wave-facing direction of the buoy was a critical factor affecting the mooring and energy-harvesting performances, while the number of anchor lines had little effect on them. In addition, a method to evaluate the motion response of the buoy based on the number of effective excitations and a method to evaluate the comprehensive performance based on the cloud chart are proposed. The mooring mode and wave condition combination that obtained the optimal mooring and energy-harvesting performances for the ERWEC was determined. This paper provides a novel perspective on how to balance the efficiency and reliability of wave energy converters.