The growing need for sustainable and efficient power sources in underwater vehicles has led to increased exploration of alternative energy solutions. In this work, fuel cells valued for their high energy density and low environmental impact are evaluated for their potential to deliver continuous power for extended underwater missions. Additionally, two energy storage system configurations, namely batteries and supercapacitors, are analyzed based on their storage capacity and overall efficiency. To enhance energy utilization in underwater vehicles, a comparative study of various rule-based energy management strategies is carried out, incorporating experimental power consumption data and high-fidelity simulation models. This analysis aims to identify the strengths and limitations of fuel cells and storage systems in meeting the demands of underwater operations. Ultimately, the findings contribute to the ongoing development of underwater vehicle technology, with a focus on improving energy efficiency and mission endurance.

This paper presents the design and optimization of a hybrid microgrid system that integrates offshore wind energy, water-fed electrolyzers, hydrogen storage, fuel cells, and battery storage to support marine applications. The proposed system is intended to supply a sustainable and economically viable energy source for docking stations servicing both surface and underwater electric vehicles. Offshore wind power is utilized to drive the electrolysis of seawater, producing hydrogen that is stored and later converted into electricity through fuel cells. This approach ensures stable energy availability despite the intermittent nature of wind, while leveraging clean water resources. Battery storage complements the system by addressing short-term load variations. An intelligent energy management strategy coordinates the various subsystems to enhance reliability and efficiency. Simulation results confirm the system’s effectiveness as a robust, green power solution for marine infrastructure.

Tidal stream turbines (TSTs) are crucial for renewable energy generation but face challenges from marine biofouling , significantly impacting their efficiency. Traditional methods for predicting performance and detecting biofouling rely on empirical models and manual inspections, which are often time-consuming and less accurate. This study introduces RegStack, a novel machine learning-based ensemble model, to enhance the prediction of power and thrust coefficients ( C P and C T ) and accurately classify biofouling levels in TSTs. Unlike conventional models, RegStack integrates L1 and L2 regularization into a stacking framework, improving robustness, generalization, and interpretability. The model dynamically balances the strengths of multiple regression and classification algorithms , optimizing predictive accuracy while mitigating overfitting. Comprehensive experiments were conducted using an extensive dataset of tidal stream turbine performance metrics under varying operational and environmental conditions. The RegStack model outperformed conventional approaches, achieving a coefficient of determination ( R 2 ) of 0.989 for performance predictions, with minimal mean absolute error (MAE) and mean squared error (MSE). Additionally, the model achieved 98.39% classification accuracy , with precision and recall of 0.97, and an F1-score of 0.97 in biofouling detection, demonstrating its effectiveness in real-time turbine health monitoring. By providing an automated, data-driven alternative to traditional methods, this study underscores the potential of advanced machine learning techniques in optimizing TST operations, reducing maintenance costs, and enhancing the reliability of marine renewable energy systems. The proposed RegStack model offers a scalable framework applicable to other renewable energy technologies , supporting sustainable energy advancements.

Biofouling poses a significant challenge to the efficiency and performance of tidal stream turbines. This study proposes a novel approach utilizing Fast R-CNN for biofouling detection and coverage estimation in tidal stream turbines. We use a real biofouling dataset collected from four turbines installed on the pier of Ikitsuki Bridge in Hirado, Nagasaki, Japan, over 8 months, containing both clean and biofouled turbine images. Employing various augmentation techniques, we enhanced the dataset’s robustness. Our method achieved a remarkable 94% accuracy in detecting and classifying biofouled turbine blades. Additionally, using the OpenCV library in Python, we estimated the extent of biofouling coverage on turbine blades, offering valuable insights for maintenance and optimization efforts and assisting informed decision-making on strategies for tidal stream turbine maintenance and performance optimization.

This study addresses the biofouling challenges in Tidal Stream Turbines (TSTs) to ensure their reliable and optimal operation. In this context, it is proposed an effective methodology employing a soft voting ensemble transfer learning-based approach for the detection and extent classification of biofouling. The proposed framework incorporates essential components such as data augmentation and pre-processing, including image resizing and data segmentation, forming a comprehensive video image-based approach. To overcome the constraint of limited data, experimental investigations were conducted, resulting in the acquisition of two datasets: one from the TST platform at Shanghai Maritime University (SMU) and the other from the tidal turbulence test facility at Lehigh University (LU). The three prominent convolutional neural network models, namely Visual Geometry Group (VGG), Residual Network (ResNet) and MobileNet, trained on these datasets, demonstrate precise detection and classification of turbine conditions, achieving an accuracy of 83% for the SMU dataset and 90% for the LU dataset. The noted disparity in accuracy for the SMU dataset is attributed to its smaller size, highlighting the significant impact of dataset scale on classification performance. This study provides valuable insight into the development of effective biofouling detection and classification strategies for TST systems. • Provide a soft voting ensemble transfer learning-based tool for real-time biofouling detection and estimation. • Data augmentation using rotation, scaling, flipping, zooming, and brightening of the cropped input images. • Data pre-processing including image resizing and data segmentation using the segment anything model.

This paper investigates the use of transfer learning models for detecting and classifying biofouling extent in tidal stream turbines. It provides a comprehensive exploration of a soft voting ensemble approach to deal with a multiclass image detection problem. In this context, three well-known convolutional neural network models, namely VGG16, ResNet50, and MobileNetV2, are the primary focus of the proposed study and are trained on an experimental dataset issued from the Shanghai Maritime University tidal stream turbine platform.

This paper proposes to map a machine learning path forward for tidal stream turbines biofouling detection and estimation. The proposed review covers an overview of biofouling and its impact on tidal stream turbines, current techniques for detecting and estimating biofouling, recent developments, and challenges in the field, as well as several promising prospects for biofouling detection and estimation.

This paper proposes a fault-tolerant control scheme for PWM inverter-fed induction motor-based electric vehicles. The proposed strategy deals with power switch (IGBTs) failures mitigation within a reconfigurable induction motor control. In a vehicle context, 4-wire and 4-leg PWM inverter topologies are investigated and their performances discussed. Two topologies exploit the induction motor neutral accessibility for fault-tolerant purposes. The 4-wire topology uses then classical hysteresis controllers to account for the IGBT failures. The 4-leg topology, meanwhile, uses a specific 3D space vector PWM to handle vehicle requirements in terms of size (DC bus capacitors) and cost (IGBTs number). Experiments on an induction motor drive and simulations on an electric vehicle are carried-out using a European urban driving cycle to assess the FTC scheme performance and effectiveness.

This paper deals with the sensorless control of a doubly-fed induction generator (DFIG) based wind turbine. The sensorless control scheme is based on a high-order sliding mode (HOSM) observer to estimate the DFIG rotational speed. Indeed, high-order sliding mode observers provide theoretically finite time exact state observation and estimation of absolutely continuous unknown inputs. The proposed global control strategy combines an MPPT using a high-order sliding mode speed observer and a high-order sliding mode for the DFIG control. This strategy presents attractive features such as chattering-free behavior, finite reaching time, robustness and unmodeled dynamics (generator and turbine). Simulations using the wind turbine simulator FAST on a 1.5-MW three-blade wind turbine are carried out for the validation of the proposed sensorless control strategy.