Automatic defect classification in photovoltaic (PV) modules is gaining significant attention due to the limited application of manual/visual inspection. However, the automatic classification of defects in crystalline silicon solar cells is a challenging task due to the inhomogeneous intensity of cell cracks and complex background. The present study is carried out for automatic defects classification of PV cells in electroluminescence images. Two machine learning approaches, features extraction‐based support vector machine (SVM) and convolutional neural network (CNN) are used for the solar cell defect classifications. Suitable hyperparameters, algorithm optimisers, and loss functions are used to achieve the best performance. Solar cell defects are divided into seven classes such as one non‐defective and six defective classes. Feature extraction algorithms such as histograms of oriented gradients (HOG), KAZE, Scale‐Invariant Feature Transform (SIFT) and speeded‐up‐robust features (SURF) are used to train the SVM classifier. Finally, the performance results are compared. It is concluded that CNN's accuracy for solar cell defect classification is 91.58% which outperforms the state‐of‐the‐art methods. With features extraction‐based SVM, accuracies of 69.95, 71.04, 68.90, and 72.74% are obtained for HOG, KAZE, SIFT, and SURF, respectively. The present study may contribute to making a PV system more efficient for classifying defects to improve the power system efficiency.

The aim of this work was to work out the mechanical stresses within transformer resulting from the extreme short-circuit currents. The forces and stresses set up in transformer windings as the result of exterior or interior short-circuits or of switching operations, are measured in detail. A variety of arrangements of windings in large power transformers are described. Points at which mostly high mechanical stresses take place in concentric windings are discussed in detail. Analytical and FEM calculations for individual short circuit forces, axial and radial have been discussed. The result was then compared with actual measurements on a prototype 20MVA 132/11.5kV power transformer [15]. Various failure mechanisms due to these forces have been discussed. Design parameters are also discussed, whose values determine the maximum stresses which may occur in any part of the transformer. Effects of irregularity in various parts and various properties of materials have been studied and the usage of appropriate material for withstanding the dynamic effects of SC is discussed. Effect of workmanship errors on short circuit withstand capability has also elucidated. Finally, a complete model is developed

Work productivity is one of the most important economic measures in the manufacturing industry. However, the physical, psychosocial, and individual risk factors of an industrial work environment affect workers’ physical or mental health, resulting in work productivity loss, absenteeism, and presenteeism. Therefore, this study aims to identify the most critical risk factors and develop the statistical models for predicting work productivity loss, absenteeism, and presenteeism of the garment industry workers. A sample of 224 sewing machine operators was taken for data collection through observation and self-reported studies. The results indicated that the average work productivity loss, absenteeism and presenteeism was 38.21%, 2.35% and 37.23%, respectively. Finally, the statistical models of work productivity loss, absenteeism and presenteeism was developed using multiple linear regression with precision of 69.9%, 53.7% and 84.0%, respectively. Hence, this study will help garment industries to improve their work productivity by taking initiatives based on the developed models.