This paper presents the design and characterization of a compact broadband antenna and its MIMO configuration for 28 GHz 5G applications. The antenna was designed using Rogers RT/5880 with a thickness of 1.575 mm and has an overall compact size of 30 mm × 30 mm. The design methodology was initiated by designing a compact conventional microstrip antenna for 28 GHz. Afterward, the rectangular slots were utilized to improve the impedance bandwidth so that antenna covers the globally allocated 28 GHz band spectrum for 5G applications. Furthermore, a compact 2 × 2 MIMO antenna with polarization diversity is designed for high channel capacity systems. The mutual coupling between the closely spaced antenna elements is reduced by using two consecutive iterations of defected ground structure (DGS). The proposed MIMO antenna system offers broad bandwidth, high gain, low mutual coupling, and low envelope correlation coefficient along with high diversity gain, low mean effective gain, and low channel capacity loss. Moreover, the proposed been compared with the state-of-the-art MIMO antenna proposed for 28 GHz application to demonstrate worth of the presented design.

The reliable operation of power transmission systems is essential for maintaining the stability and efficiency of the electrical grid. Rapid and accurate detection of faults in transmission lines is crucial for minimizing downtime and preventing cascading failures. This research presents a novel approach to fault detection and classification in transmission lines employing 2D Convolutional Neural Networks (2D-CNN).The proposed methodology leverages the inherent spatial characteristics of fault signals, converting them as 2D scalogram images for input to the CNN model. By converting fault signals into scalogram representations, the network can capture both temporal and frequency domain features, enabling a more comprehensive analysis of fault patterns. The 2D-CNN architecture is designed to automatically learn hierarchical features, allowing for effective discrimination between different fault types. To evaluate the performance of the proposed approach, extensive simulations and experiments were conducted using MATLAB/SIMULINK modeled transmission line data. The results demonstrate the superior fault detection accuracy and classification capabilities of the 2D-CNN model. The performance of the proposed model is evaluated using 10-fold cross-validation, and its effectiveness is assessed by comparing it with current state-of-the-art techniques. Proposed 2D-CNN model has evidenced an accuracy of 99.9074 with ideal dataset for 12- class fault classification and performing consistently in presence of noise, having an accuracy of 99.629 %,99.72 % and 99.814 % in 20.30 and 40 dB noises respectively. The proposed model also verified in high resistance fault condition. The model exhibits robustness to noise and is capable of generalizing well to various fault scenarios. The proposed methodology offers a scalable and efficient solution for transmission line fault analysis, paving the way for the integration of advanced machine learning techniques into the operation and maintenance of power transmission infrastructure.

The induction motor (IM) is considered to be one of the most important types of motors used in industries. A sudden failure in this machine can lead to unwanted downtime, with consequences in costs, product quality, and safety. Over the last decade, several methods and techniques have been proposed to diagnose and detect faults in induction machines. In this paper, we present the development of a new algorithm based on the combination of both the Park’s vector approach (PVA) and the extended Park’s vector approach (EPVA) for broken rotor bars (BRBs) fault detection and identification. This fault can be detected using the PVA by monitoring the thickness and orientation of the park’s vector pattern and using EPVA by identifying specific spectral components related to the fault. For ability evaluation of our suggested algorithm, simulations and experiments are conducted and presented. The obtained results demonstrate that the proposed algorithm is accurate and effective and can be extensively used in IM fault detections and identifications.

This article presents the selection of location and sizing of multiple distributed generators (DGs) for boosting performance of the radial distribution system in the case of constant power load flow and constant impedance load flow. The consideration of placing and sizing of DGs is to meet the load demand. This article tries to overcome the limitations of existing techniques for determining the appropriate location and size of DGs. The selection of DG location is decided in terms of real power losses, accuracy, and sensitivity. The size of DG is measured in terms of real and reactive power. Both positioning and sizing of DG are analyzed with the genetic algorithm and the heuristic probability distribution method. The results are compared with other existing methods such as ant-lion optimization algorithm, coyote optimizer, modified sine-cosine algorithm, and particle swarm optimization. Further, the power quality improvement of the network is assessed by positioning D-STATCOM, and its location is decided on the basis of the nearby bus having poor voltage profile and high total harmonic distortion (THD). The switching and controlling of D-STATCOM are assessed with fuzzy logic controller (FLC) for improving the performance parameters such as voltage profile and THD at that particular bus. The proposed analytical approach for the system is tested on the IEEE 33 bus system. It is observed that the performance of the system with the genetic algorithm gives a better solution in comparison to heuristic PDF and other existing methods for determining the optimal location and size of DG. The introduction of D-STATCOM into the system with FLC shows better performance in terms of improved voltage profile and THD in comparison to existing techniques.

In this paper, the consensus problem is addressed for multi-agent systems. The dynamics of each agent contain unknown uncertain/nonlinear terms and unknown time delays. A type-3 fuzzy logic system is developed to tackle the effect of unknown dynamics and design a hybrid controller. The policy scheme involves two control signals for the stabilization of the approximation and consensus error of each agent dynamic. To this end, based on the concept of the model predictive control approach, the constrained control laws are designed and updated at each time step. The simulations results portray the error signals. Feasibility, appropriate convergence, and proper transient response are the main merits of the suggested method.

Among the polymeric family, high-temperature-vulcanized silicone rubber (HTV-SR) is the most deployed material for high voltage insulation applications. However, in an outdoor environment, due to contamination and wetting-induced dry band arcing, consequently SR experiences surface tracking and erosion. From a practical standpoint, the tracking and erosion performance under multi-stress aging is required to be known. It is in that context that the present study was undertaken to measure and analyze the effect of multi-stress aging on tracking and erosion performance. Composite samples of SR having different filler concentrations of silica and alumina trihydroxide (ATH) were aged in a multi-stress chamber for a period of 5000 h, and after that their electrical tracking performance was studied. Simultaneously, unaged samples were also exposed to tracking test for comparison. To conduct this test, the inclined plane testing technique was used in accordance with IEC-60587. All samples exposed to tracking test were analyzed using different diagnostic and measuring techniques involving surface leakage current measurement, Fourier transform infrared spectroscopy (FTIR), thermal stability and hydrophobicity classification. Experimental results shown that the tracking lifetime increased through incorporation of silica and ATH fillers in the SR. Amongst all test samples, two samples designated as filled with 2% nano silica and 20% micro silica/ATH exhibited greater resistance to tracking. This was attributed to the optimum loading as well as better dispersion of the fillers in the polymer matrix. The presence of nano-silica enhanced time-to-tracking failure, owing to both improved thermal stability and enhanced shielding effect on the surface of nanocomposite insulators.