Wind energy has seen great development during the past decade. However, wind turbine availability and reliability, especially for offshore sites, still need to be improved, which strongly affect the cost of wind energy. Wind turbine operational cost is closely depending on component failure and repair rate, while fault detection and isolation will be very helpful to improve the availability and reliability factors. In this paper, an efficient machine learning method, random forests (RFs) in combination with extreme gradient boosting (XGBoost), is used to establish the data-driven wind turbine fault detection framework. In the proposed design, RF is used to rank the features by importance, which are either direct sensor signals or constructed variables from prior knowledge. Then, based on the top-ranking features, XGBoost trains the ensemble classifier for each specific fault. In order to verify the effectiveness of the proposed approach, numerical simulations using the state-of-the-art wind turbine simulator FAST are conducted for three different types of wind turbines in both the below and above rated conditions. It is shown that the proposed approach is robust to various wind turbine models including offshore ones in different working conditions. Besides, the proposed ensemble classifier is able to protect against overfitting, and it achieves better wind turbine fault detection results than the support vector machine method when dealing with multidimensional data.

The observation and detection of the subsea environment urgently require large-scale and long-term observation platforms. The design and development of subsea AUVs involve three key points: the subsea-adapted main body structure, agile motion performance that adapts to complex underwater environments, and underwater acoustic communication and positioning technology. This paper discusses the development and evolution of subsea AUVs before proposing solutions to underwater acoustic communication and positioning navigation schemes. It also studies key technologies for the agile motion of subsea AUVs and finally gives an example of a solution for implementing underwater AUVs, i.e., the disk-shaped autonomous underwater helicopter (AUH). This paper will provide guidance for the design of subsea AUVs and the development of corresponding observation and detection technologies.

The vibration signals for offshore wind-turbine high-speed bearings are often contaminated with noises due to complex environmental and structural loads, which increase the difficulty of fault detection and diagnosis. In view of this problem, we propose a fault-diagnosis strategy with good noise immunity in this paper by integrating the two-dimensional convolutional neural network (2DCNN) with random forest (RF), which is supposed to utilize both CNN’s automatic feature-extraction capability and the robust discrimination performance of RF classifiers. More specifically, the raw 1D time-domain bearing-vibration signals are transformed into 2D grayscale images at first, which are then fed to the 2DCNN-RF model for fault diagnosis. At the same time, three procedures, including exponential linear unit (ELU), batch normalization (BN), and dropout, are introduced in the model to improve feature-extraction performance and the noise immune capability. In addition, when the 2DCNN feature extractor is trained, the obtained feature vectors are passed to the RF classifier to improve the classification accuracy and generalization ability of the model. The experimental results show that the diagnostic accuracy of the 2DCNN-RF model could achieve 99.548% on the CWRU high-speed bearing dataset, which outperforms the standard CNN and other standard machine-learning and deep-learning algorithms. Furthermore, when the vibration signals are polluted with noises, the 2DCNN-RF model, without retraining the model or any denoising process, still achieves satisfying performance with higher accuracy than the other methods.

Navigation safety is one of the primary operational requirements for unmanned surface vehicles (USVs) in a complex marine environment, mainly guaranteed by a reliable path planning system for collision avoidance. This work proposes a novel weighted sum multi-objective optimisation strategy for USV collision avoidance path planning in restricted waters. In particular, the coefficients of different objectives could be tuned to emphasise the most critical design consideration under varying navigation scenarios. Moreover, in addition to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the terrain and weather constraints were also considered in the path planning system. The proposed USV collision avoidance path planning framework’s effectiveness was demonstrated through numerical simulations and hardware-in-the-loop (HIL) tests. The numerical simulation results indicate that the proposed method could avoid collision with dynamic and static obstacles, and it is also adaptive to different navigation restrictions and preferences. Moreover, a USV navigation platform was established by incorporating true Automatic Identification System (AIS) signals, and HIL tests were performed with real-time AIS data in a water channel in the Zhoushan archipelago. The results demonstrate that the proposed USV path planning strategy is applicable in restricted waters with complex terrains and weather constraints.

Floating offshore wind turbines (FOWTs) are subject to intensive structural loads due to the extra degrees of freedom (DOF) of the floating platform, which may shorten the fatigue lifetime of critical wind turbine structures. Integrating wave energy converters (WECs) into FOWTs could potentially help improve both overall energy capture and platform dynamic responses and, thus, is expected to reduce the levelized cost of energy (LCOE). In this work, a novel hybrid wind-wave energy platform consisting of a semi-submersible FOWT and three heaving-type WECs is proposed, and the feasibility of reducing FOWT dynamic responses and fatigue loads by integrating heaving-type WECs with different PTO control schemes is investigated. More specifically, the aero-hydro-servo-elastic-mooring coupled numerical model is established, and a preliminary study is performed to evaluate the dynamic responses and power production of the hybrid platform under various environmental conditions. Particularly, the two kinds of PTO control strategies have been comparatively studied, which have shown that the active bang-bang control could effectively suppress the platform heave and pitch motion by up to 34.6 % and 17.1%, respectively. Moreover, the tower-base fatigue damage equivalent load (DEL) has been reduced by up to 11.21%, and the system power production could be increased by almost 6%. Therefore, it is shown that integrating heaving-type wave energy converters with bang-bang control is able to effectively reduce the dynamic responses and fatigue loads of semi-submersible FOWT while absorbing additional wave energy at the same time.