Worldwide, Nuclear Power Plants (NPPs) must have higher security protection and precise fault detection systems, especially underground power cable faults, to avoid causing national disasters and keep on safe national ratios of electricity production. Hence, this paper proposes an automatic, effective, and accurate Deep Learning (DL)-based fault classification and location technique for these cables via a One-dimensional Convolutional Neural Network (1D-CNN) and a Binary Support Vector Machine (BSVM). The proposed approach includes four main steps: data collection, feature extraction and reduction, fault detection, and fault classification and location. Signal collection from the underground cable’s sending end is performed via the Alternating Transient Program/Electromagnetic Transient Program (ATP/EMTP). Feature extraction and reduction are performed via Fractional Discrete Cosine Transform (FrDCT) and Singular Value Decomposition (SVD) methods. Fault detection is performed through leveraging BSVM with the linear Kernel method in the third step. Finally, this permits 1D-CNN to classify the fault type and locate it. Simulation results confirmed the efficiency of our proposed method, especially for 11kV underground cable faults, including different fault resistances and inception angles. Moreover, the proposed technique is applicable in real-time scenarios with a 99.6% accuracy rate, 0.15sec lowest execution time, and 0.095% maximum error rate for fault location at fractional factor ( $\alpha $ ) equals to 0.8.

: Early diagnosis of plant diseases is crucial for preventing plagues and mitigating their effects on crops. The most precise automatic methods for identifying plant diseases using images of plant fields are powered by deep learning. Big image datasets should always be gathered and annotated for these methods to work, which is often not technically or financially feasible. This paper offers one-shot learning (OSL) techniques for plant disease classification with limited datasets utilizing Siamese Neural Network (SNN). There are five different crop kinds in the dataset: grape, wheat, cotton, cucumber, and corn. Five sets of images showing both healthy and diseased crops are used to represent each of the new crops. The dataset's includes 25 classes with 875 leaf images. Data augmentation techniques are used to enhance the size and dimension of the plant leaf disease image dataset. To provide effective segmentation, this paper provides a unique method for region-based image segmentation that divides an image into its most prominent regions. It also addresses issues with earlier region-based segmentation methods. SVM-based classifiers have better generalization properties as their efficiency does not depend on the number of features. Such merit is beneficial in primary diagnostics decisions to check if the input image is included in the database or not to reduce the consumed time. OSL was applied and compared to standard fine-tuning transfer learning utilizing Siamese networks and triplet loss. Siamese provides superior classification accuracy and localization accuracy with minimal errors than other approaches. The proposed approach has a total processing time of 5 ms, which makes it appropriate for real-time applications. In terms of specificity, sensitivity, precision, accuracy, MCC, and F-measure, the proposed approach beats all current machine learning algorithms for small training sets.

Faults can seriously damage high-voltage (HV) power systems, particularly if they occur on the long overhead transmission line (OHTL) that connects the nuclear power plant (NPP) to the electrical grid. Finding OHTL problems quickly and accurately is essential for the economy, safety, and dependability of the HV power systems. It is essential to pinpoint the problematic phase to avoid unneeded power outages. Thus, one of the most crucial research challenges is now how to identify, classify, and locate OHTL defects. In this study, transient current with high frequency oscillations that arise immediately after a defect at the sending end is investigated in a single-circuit, single-side fed Egyptian 500-kV HV long OHTL. Asymmetric and symmetric faults and locations are also represented in the Alternative Transients Program-Electro Magnetic Transients Program (ATP/EMTP) simulation model under varying fault resistance and inception angles. The proposed solution in this paper is an Optimized Support Vector Machine (OSVM), whose characteristics are optimized via a mutant particle swarm optimization (MPSO) method to detect 500 kV long OHTL faults. The localizer model is also built for practical applications, including power system noise contaminating fault signals. The findings prove that the suggested approach locates the fault in 0.012 seconds from the start of the event, with a 0.0098 percent average percentage error, and without being impacted by differences in fault distance, fault resistance, noise, or fault inception angle. Additionally, the optimised classifier reaches a 99.85% accuracy rate, enhancing line system dependability and advancing nuclear system development.