Aging is known to exert various non-uniform effects on photovoltaic (PV) modules within a PV array that consequently can result in non-uniform operational parameters affecting the individual PV modules, leading to a variable power output of the overall PV array. This study presents an algorithm for optimising the configuration of a PV array within which different PV modules are subject to non-uniform aging processes. The PV array reconfiguration approach suggests maximising power generation across non-uniformly aged PV arrays by merely repositioning, rather than replacing, the PV modules, thereby keeping maintenance costs to a minimum. Such a reconfiguration strategy demands data input on the PV module electrical parameters so that optimal reconfiguration arrangements can be selected. The algorithm repetitively sorts the PV modules according to a hierarchical pattern to minimise the impact of module mismatch arising due to non-uniform aging of panels across the array. Computer modelling and analysis have been performed to assess the efficacy of the suggested approach for a variety of dimensions of randomly non-uniformly aged PV arrays (e.g., 5 × 5 and 7 × 20 PV arrays) using MATLAB. The results demonstrate that enhanced power output is possible from a non-uniformly aged PV array and that this can be applied to a PV array of any size.
Composite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts with a valuable and rapid mechanism to monitor the health and safety of composite materials. Although many attempts have been made to develop damage detection techniques and make operations more efficient, there is still a need to develop/improve existing methods. Pulsed thermography (PT) technology was used in this study to obtain healthy and defective data sets from custom-designed composite samples having similar dimensions but different thicknesses (1.6 and 3.8). Ten carbon fibre-reinforced plastic (CFRP) panels were tested. The samples were subjected to impact damage of various energy levels, ranging from 4 to 12 J. Two different methods have been applied to detect and classify the damage to the composite structures. The first applied method is the statistical analysis, where seven different statistical criteria have been calculated. The final results have proved the possibility of detecting the damaged area in most cases. However, for a more accurate detection technique, a machine learning method was applied to thermal images; specifically, the Cube Support Vector Machine (SVM) algorithm was selected. The prediction accuracy of the proposed classification models was calculated within a confusion matrix based on the dataset patterns representing the healthy and defective areas. The classification results ranged from 78.7% to 93.5%, and these promising results are paving the way to develop an automated model to efficiently evaluate the damage to composite materials based on the non-distractive testing (NDT) technique.
Diagnosis and prognosis of failures for aircrafts' integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. This paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of active thermography techniques in the aerospace industry. Furthermore, as the use of unmanned aerial vehicles (UAVs) for the remote inspection of large and/or difficult access areas has significantly grown in the last few years thanks to their flexibility of flight and to the possibility to carry one or several measuring sensors, the aim to use a UAV active thermography system for the inspection of large composite aeronautical structures in a continuous dynamic mode is proposed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.