Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral imaging (HSI) has been widely used in a wide range of real-world application areas due to the in-depth spectral information stored within each pixel. Noticeably, the detailed features, i.e., a nonlinear correlation between the obtained spectral data and the correlating HSI data object, generate efficient classification results that are complex for traditional techniques. Deep learning (DL) has recently been validated as an influential feature extractor that efficiently identifies the nonlinear issues that have arisen in various computer vision challenges. This motivates using DL for HSIC, which shows promising results. This survey provides a brief description of DL for HSIC and compares cutting-edge methodologies in the field. We will first summarize the key challenges for HSIC, and then, we will discuss the superiority of DL and DL ensemble in addressing these issues. In this article, we divide state-of-the-art DL methodologies and DL with ensemble into spectral features, spatial features, and combined spatial–spectral features in order to comprehensively and critically evaluate the progress (future research directions as well) of such methodologies for HSIC. Furthermore, we will take into account that DL involves a substantial percentage of labeled training images, whereas obtaining such a number for HSI is time and cost consuming. As a result, this survey describes some methodologies for improving the classification performance of DL techniques, which can serve as future recommendations.

The increase in the internal temperature of high voltage electrical instruments is due to a variety of factors, particularly, contact problems; environmental factors; unbalanced loads; and cracks in the high voltage current transformers, voltage transformers, insulators, or terminal junctions. This increase in the internal temperature can cause unusual disturbances and damage to high voltage electrical equipment. Therefore, early prevention measures of thermal anomalies in equipment are necessary to prevent high voltage equipment failure that might shut down the whole grid system. In this article, we propose a novel non-destructive approach to defect analysis in high voltage equipment by taking advantage of the infrared thermography and the deep learning (DL) approach from the machine learning paradigm. The infrared images of the components were captured using the FLIR T630 without disturbing the operations of the power grid. In the first stage, rich features maps from the convolutional layers of the AlexNet pretrained model were extracted. After feature extraction, the random forest (RF) and support vector machines (SVM) were trained for learning of the defective and non-defective high voltage electrical equipment. In an experimental analysis, the RF optimally learned the separation between defective and non-defective equipment with greater than 96% accuracy, outperforming all the other comparative approaches for deep and nondeep features. The proposed approach based on the RF is reliable and shows its efficacy for fault detection in high voltage electrical equipment.

A variety of reasons, specifically contact issues, irregular loads, cracks in insulation, defective relays, terminal junctions and other similar issues, increase the internal temperature of electrical instruments. This results in unexpected disturbances and potential damage to power equipment. Therefore, the initial prevention measures of thermal anomalies in electrical tools are essential to prevent power-equipment failure. In this article, we address this initial prevention mechanism for power substations using a computer-vision approach by taking advantage of infrared thermal images. The thermal images are taken through infrared cameras without disturbing the working operations of power substations. Thus, this article augments the non-destructive approach to defect analysis in electrical power equipment using computer vision and machine learning. We use a total of 150 thermal pictures of different electrical equipment in 10 different substations in operating conditions, using 300 different hotspots. Our approach uses multi-layered perceptron (MLP) to classify the thermal conditions of components of power substations into “defect” and “non-defect” classes. A total of eleven features, which are first-order and second-order statistical features, are calculated from the thermal sample images. The performance of MLP shows initial accuracy of 79.78%. We further augment the MLP with graph cut to increase accuracy to 84%. We argue that with the successful development and deployment of this new system, the Technology Department of Chongqing can arrange the recommended actions and thus save cost in repair and outages. This can play an important role in the quick and reliable inspection to potentially prevent power substation equipment from failure, which will save the whole system from breakdown. The increased 84% accuracy with the integration of the graph cut shows the efficacy of the proposed defect analysis approach.

This paper presents the power loss model analysis and efficiency of three-level neutral-point-clamped (3L-NPC) inverter which is widely employed in solar photovoltaic energy conversion system. A silicon carbide (SiC) 3L-NPC inverter is developed in this study by employing wide bandgap semiconductor power devices such as SiC MOSFET and SiC diode (SiC D). These devices are used due to their superior characteristics over silicon (Si) semiconductor devices for the reduction of inverter power losses, and as a result, improving efficiency at high switching frequency. Accurate and detailed power loss calculation formula and power loss distribution over switching devices of the SiC 3L-NPC inverter are derived according to the modulation technique and inverter operation. The switching energy loss of SiC MOSFET is then measured and determined experimentally via inductive clamp double pulse test (DPT) at real working condition of the circuit. Afterward, this experimental data is used in the thermal description file of the device’s library of PLECS simulation software to determine the total power loss of SiC 3L-NPC inverter. The developed simulation model replicates the real operating conditions of the 3L-NPC inverter. This method gives results close to the practical test. Finally, the power loss of SiC 3L-NPC inverter is measured and compared with the theoretical results. Furthermore, SiC MOSFET and SiC D are employed to achieve high system efficiency at high switching frequency. The results verify the features of SiC 3L-NPC inverter, the corresponding modulation technique used and their effects on reducing and improving power loss in solar SiC photovoltaic inverters.

The paper presents a temperature gradient-based method for device state evaluation, taking the insulated-gated bipolar transistor (IGBT) modules as an example investigation. First, theoretical basis of this method is presented and the results from example calculation on temperature gradient indicate that the increased thermal resistance and power loss of IGBT modules would increase the temperature gradient. Then, an electrical-thermal-mechanical finite-element method model of IGBT modules, which takes the material temperature-dependent characteristic into account, is utilized to estimate the temperature gradient distribution for both healthy and fatigue conditions. It is found that the temperature gradient varies with power loss. Furthermore, both the experimental and simulation investigation on the temperature gradient for different conditions were conducted, and it is concluded that the temperature gradient can not only track the change of power loss, but also have a better sensitivity compared with temperature distribution. In addition, the temperature gradient can reflect the defects location and distinguish failures degree. In the end, the influence on the temperature gradient distribution caused by solder fatigue, void, and delamination is discussed.