Multi-vector smart local energy systems are playing an increasingly importantly role in the fast-track decarbonisation of our global energy services. An emergent contributor to global decarbonisation is green hydrogen. Green hydrogen can remove or reduce the burden of electrification of heat and transport on energy networks and provide a sustainable energy resource. In this paper, we explore how to optimally design a standalone hybrid green power system (HGPS) to supply a specific load demand with on-line charging of Electric Vehicles (EV). The HGPS includes wind turbine (WT) units, photovoltaic (PV) arrays, electrolyser and fuel cell (FC). For reliability analysis, it is assumed that WT, PV, DC/AC converter, and EV charger can also be sources of potential failure. Our methodology utilises a particle swarm optimization, coupled with a range of energy scenarios as to fully evaluate the varying interdependences and importance of economic and reliability indices, for the standalone HGPS. Our analysis indicates that EV charging with peak loading can have significant impact on the HGPS, resulting in significant reductions in the reliability indices of the HGPS, therefore enhance the operation of HGPS and reduces the overall cost. Our analysis demonstrates the importance of understanding local demand within a multi-vector optimization framework, as to ensure viable and resilient energy services.

The processing of aerial images taken by drones is a challenging task due to their high resolution and the presence of small objects. The scale of the objects varies diversely depending on the position of the drone, which can result in loss of information or increased difficulty in detecting small objects. To address this issue, images are either randomly cropped or divided into small patches before training and inference. This paper proposes a defect detection framework that harnesses the advantages of slice-aided inference for small and medium-size damage on the surface of wind turbine blades. This framework enables the comparison of different slicing strategies, including a conventional patch division strategy and a more recent slice-aided hyper-inference, on several state-of-the-art deep neural network baselines for the detection of surface defects in wind turbine blade images. Our experiments provide extensive empirical results, highlighting the benefits of using the slice-aided strategy and the significant improvements made by these networks on an ultra high-resolution drone image dataset.

Commonly in Malaysia, the road surfaces deteriorate over time which results to potholes. With the increasing number of potholes on the road, it has become a road hazard, potentially harming the safety of the Malaysian citizens. Potholes generally will cause drivers to skew away from their original direction which may result in vehicle damages and/or road accidents. Computer vision has improved significantly in the past decade to improve the technologies that require image processing, such as pothole detection. However, it has several limitations in pothole detection as potholes have inconsistent shapes, making it difficult to obtain an accurate prediction. Another limitation is the speed of the detection algorithm is not able to predict the pothole in real time. In this paper, the use of computer vision technology on vehicles is presented as a form of solution to detect the potholes in real time. A deep learning model based on Convolutional Neural Networks, YOLOv5 is found to improve the accuracy of the prediction as compared to past results. The findings on the trained YOLOv5 model have a mAP@0.5 of 80.8 %, 82.2 % and 82.5 % on the YOLOv5m6, YOLOv5s6 and YOLOv5n6 respectively. The trained YOLOv5n6 model was chosen to be the main image processing model for its robust performance to size ratio. The results were able to demonstrate the actual deployment of the trained YOLOv5n6 model on the road in real time with distance estimation.

Microalgae present an enticing alternative to conventional fossil fuel-dependent technologies for producing hydrogen, offering an intriguing and sustainable energy source. Numerous strains of microalgae are under investigation for their capacity to generate hydrogen, alongside various techniques and breakthroughs being developed to optimize the process. However, significant hurdles must be addressed for commercial viability, including the high manufacturing costs and the necessity for efficient harvesting and sorting methods. This paper delves into several aspects concerning hydrogen synthesis in algae, encompassing microalgae anatomy and physiology, hydrogen synthesis via photosynthesis and dark fermentation, and the integration of microalgal hydrogen synthesis with other renewable energy sources. The potential for microalgal hydrogen generation is considered pivotal in transitioning toward a future reliant on more renewable and sustainable energy sources. This review aims to serve as a valuable resource for researchers, decision-makers, and anyone interested in the advancement of environmentally conscious energy technology. The primary objective of this research paper is to scrutinize the challenges, opportunities, and potential outcomes associated with eco-friendly bio-hydrogen production through algae. It evaluates the current technological hurdles facing bio-hydrogen synthesis from algae. Graphical Highlights: Interest in developing renewable fuels, such as hydrogen from biomass, has surged due to escalating energy demands and the imperative to curtail greenhouse gas emissions. Overview of bio-hydrogen production pathway, reactor designs, and configurations for bio-hydrogen production from bio-algae were explored. Environmental, social sustainability and economic feasibility have been reviewed. Discussion: Will bio-hydrogen from bio-algae be a future renewable energy? Which is the best pathway to produce bio-hydrogen from bio-algae? Regarding greenhouse gas emissions, how does the generation of bio-hydrogen from bio-algae compare to conventional hydrogen production techniques? What difficulties lie in increasing the amount of bio-hydrogen produced by bio-algae to satisfy major energy demands?

With the widespread deployment of photovoltaic (PV) power stations, timely identification and rectification of module defects are crucial for extending service life and preserving efficiency. PV arrays, subjected to severe outside circumstances, are prone to defects exacerbated by dust accumulation, potentially leading to complex compound faults. The resemblance between individual and compound faults sometimes leads to misclassification. To address this challenge, this paper presents a novel hybrid deep learning model, ResGRU, which integrates a residual network (ResNet) with bidirectional gated recurrent units (BiGRU) to improve fault diagnostic accuracy. Additionally, a Squeeze-and-Excitation (SE) module is incorporated to enhance relevant features while suppressing irrelevant ones, hence improving performance. To further optimize inter-class separability and intra-class compactness, a center loss function is employed as an auxiliary loss to enhance the model’s discriminative capacity. This proposed method facilitates the automated extraction of fault features from I-V curves and accurate diagnosis of individual faults, partial shading scenarios, and compound faults under varying levels of dust accumulation, hence aiding in the formulation of efficient cleaning schedules. Experimental findings indicate that the suggested model achieves 99.94% accuracy on pristine data and 98.21% accuracy on noisy data, markedly surpassing established techniques such as artificial neural networks (ANN), ResNet, random forests (RF), multi-scale SE-ResNet, and other ResNet-based approaches. Thus, the model offers a reliable solution for accurate PV array fault diagnosis.