Where Have the Litigants Gone?

The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups. For this reason, the classification of coral species has always required an aid from a domain expert. The objective of this paper is to develop an accurate classification model for coral texture images. Current datasets contain a large number of imbalanced classes, while the images are subject to inter-class variation. We have analyzed 1) several Convolutional Neural Network (CNN) architectures, 2) data augmentation techniques and 3) transfer learning. We have achieved the state-of-the art accuracies using different variations of ResNet on the two current coral texture datasets, EILAT and RSMAS.Comment: 22 pages, 10 figure

Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis

Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost. In this work, we develop a deep learning-based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach can achieve almost human-level precision in terms of suggested damage location and types on wind turbine blades. We further demonstrate that for relatively small training sets, advanced data augmentation during deep learning training can better generalize the trained model, providing a significant gain in precision

Optimizing photovoltaic arrays : A tested dataset of newly manufactured PV modules for data-driven analysis and algorithm development

This data article presents a comprehensive dataset comprising experimentally tested characteristics of newly manufactured photovoltaic (PV) modules, which have been collected by using a commercial PV testing system from a solar panel manufacturer company. The PV testing system includes an artificial sunlight simulator to generate input light for the PV and the outputs of the PV are tested by a professional IV tracer in a darkroom environment maintaining IEC60904–9 standard. The dataset encompasses modules with power ratings of 10 W, 85 W, and 247 W, each represented by 40 individual module records. The tested and collected characteristics of each module include open circuit voltage, short circuit current, maximum power point voltage, maximum power point current, maximum power point power, and fill factor. The motivation for this dataset lies in addressing the challenges posed by manufacturing defects and a ± 5 % manufacturing tolerance, which can lead to mismatch power losses in newly installed PV arrays. These losses result in lower current in series strings and lower voltage in parallel branches, ultimately decreasing the array's output power. The dataset serves as a valuable resource for academic research, particularly in the domain of PV array optimization. To facilitate optimization efforts, different algorithms have been explored in the literature. This dataset supports the exploration of these optimization algorithms to find solutions that enhance the position of each module within the array, consequently increasing the overall output power and efficiency of the PV system. The objective is to mitigate mismatch power losses, which, if unaddressed, can contribute to increased degradation rates and early aging of PV modules. This dataset lays the groundwork for addressing critical PV array performance and efficiency issues. In future research, this dataset can be reused to explore and implement optimization algorithms, to improve the overall output power and lifespan of newly installed PV arrays. The smart solution proposed in [1], utilizing a genetic algorithm-based module arrangement, demonstrates promising results for maximizing PV array output power using this dataset

Feasibility analysis of floating photovoltaic power plant in Bangladesh: A case study in Hatirjheel Lake, Dhaka

The installation of large-scale photovoltaic (LSPV) power plants is a solution to mitigate the national energy demand in Bangladesh. However, the land crisis is one of the key challenges for the rapid growth of ground-mounted LSPV plants in Bangladesh. The per unit cost of energy from ground-mounted PV systems is rising as a response to numerous difficulties, particularly for large-scale electricity generation. To overcome the issues with land-based PV, the floating photovoltaic (FPV) could be a viable solution. To the aspirations of the Sustainable and Renewable Energy Development Authority (SREDA), this article has investigated the feasibility of constructing a floating solar plant at Hatirjheel Lake in Dhaka, Bangladesh. The lake is an excellent spot to build an FPV plant due to its geographic location and climatic conditions inside the capital city. In this paper, the design of the plant and tariff are carried out using the PVsyst simulator. It is found that the optimum cost of energy for the plant is $ 0.0959/KWh, which is lesser than the currently operational ground-mounted PV plants in Bangladesh. Additionally, the projected 6.7 MW plant can meet 12.5 % of the local energy demand. Furthermore, the FPV plant is capable to cut off 6685 tons of CO2 annually. A reduction in power costs and environmental protection would assist the government of Bangladesh in achieving the sustainable development goals and electricity generation target of 6000 MW from solar photovoltaics by 2041 as well

The effects of non-uniformly-aged photovoltaic array on mismatch power loss : A practical investigation towards novel hybrid array configurations

One of the most important causes of a reduction in power generation in PV panels is the non-uniform aging of photovoltaic (PV) modules. The increase in the current–voltage (I–V) mismatch among the array modules is the primary cause of this kind of degradation. There have been several array configurations investigated over the years to reduce mismatch power loss (MPL) caused by shadowing, but there have not been any experimental studies that have specifically examined the impact of various hybrid array topologies taking PV module aging into consideration. This research examines the influence of the non-uniform aging scenario on the performance of solar PV modules with various interconnection strategies. Experiments have been carried out on a 4 × 10, 400 W array with 12 possible configurations, including three proposed configurations (LD-TCT, SP-LD, and LD-SP), to detect the electrical characteristics of a PV system. Finally, the performances of different module configurations are analyzed where the newly proposed configurations (SP-LD and LD-SP) show 15.80% and 15.94% higher recoverable energy (RE), respectively, than the most-adopted configuration (SP). Moreover, among the twelve configurations, the SP configuration shows the highest percentage of MPL, which is about 17.96%, whereas LD-SP shows the lowest MPL at about 4.88%