In the context of harvesting tidal stream energy, which is considered a promising source of renewable energy due to its high energy density, stability, and predictability, this paper proposes a review-based roadmap investigating the use of data-driven techniques, more specifically machine learning-based approaches, to detect and estimate the extent of biofouling in tidal stream turbines. An overview of biofouling and its impact on these turbines will be provided as well as a brief review of current methodologies and techniques for detecting and estimating biofouling. Additionally, recent developments and challenges in the field will be examined, while providing several promising prospects for biofouling detection and estimation in tidal stream turbines.

Biofouling, the accumulation of marine organisms on submerged surfaces, presents significant operational challenges across various marine industries. Traditional detection methods are labor intensive and costly, necessitating the development of automated systems for efficient monitoring. The study presented in this paper focuses on detecting biofouling on tidal stream turbine blades using camera-based monitoring. The process begins with dividing the video into a series of images, which are then annotated to identify and select the bounding boxes containing objects to be detected. These annotated images are used to train YOLO version 8 to detect biofouled and clean blades in the images. The proposed approach is evaluated using metrics that demonstrate the superiority of this YOLO version compared to previous ones. To address the issue of misdetection, a data augmentation approach is proposed and tested across different YOLO versions, showing its effectiveness in improving detection quality and robustness.

This paper deals with the energy management of a hybrid power system, which consists of a photovoltaic (PV) system, diesel generators, battery, and ultracapacitor for a mobile hospital. The proposed power system can supply energy to shelter hospitals for better treatment of patients in remote states, particularly in the event of a pandemic situation such as Coronavirus Disease 2019 (COVID-19). For this reason, a hybrid power system in which a diesel generator is used with a photovoltaic energy source for reliable availability of power supply. Moreover, battery and ultracapacitor are also integrated to obtain a hybrid power generation and storage system to ensure the smooth operation of mobile hospitals irrespective of weather conditions. A boost converter is used with PV panels to operate them in either maximum power tracking mode or power curtailment mode. The battery is connected to a bidirectional reversible DC-DC converter for direct-current (DC) bus voltage regulation and state of charge control. The ultracapacitor is associated with the battery to compensate for the peak power. The diesel generator is connected in parallel with the photovoltaic generator, battery, and ultracapacitor to continuously provide the power required by the load. The integrated operation of all generation sources and storage systems is complex for shelter hospitals. Therefore, an efficient energy management algorithm is developed to manage the continuous energy flow between different elements of the hybrid power system and mobile hospital load through the control of the power converters. Finally, validation results are presented to show the effectiveness of the proposed energy management algorithm for the hybrid power system.