The evolution of smart grid technology necessitates sophisticated methods for fault detection to ensure system reliability and efficiency. Monitoring a complex power grid with phasor measurement units (PMUs) continuously transmitting data at high velocities. Rapidly and accurately analyzing this extensive data to detect faults presents a major challenge for grid operators. This study introduces a novel approach for fault classification in smart grids by utilizing Convolutional Neural Networks (CNNs) based on the architectures of VGG16 and VGG19. VGG is capable of rapidly classifying various grid events, such as faults, generation losses, and synchronous motor switching, with high efficiency. The system detects faults swiftly, allowing operators to minimize downtime and prevent significant damage by enabling prompt responses. The study meticulously examines the performance metrics of each model, including accuracy, precision, recall, and F1 score. Evaluations reveal that the VGG16 model outperforms VGG19, achieving an impressive accuracy of 98.75% and consistent precision, recall, and F1 scores of 0.99. In contrast, the VGG19 model attained a lower accuracy of 95.00%, with slightly diminished performance metrics. These findings highlight the efficacy of advanced deep learning techniques in improving fault detection accuracy within smart grid systems, suggesting that VGG16 offers a more reliable and accurate solution compared to VGG19.

Microgrids are localised power system that use local power generation to supply electricity to nearby loads. This idea has gained popularity with the development of battery energy storage system (BESS) and the emergence of renewable energy sources (RES). The integration of electric vehicles (EVs) into the grid has made it possible to integrate batteries and RES without significantly altering the system. The operation of microgrids is has discussed in this study with a special emphasis on power flow control during system disturbances and transitions. This article describes an algorithm designed to optimise the regulation of power, voltage, and frequency in a microgrid that includes EVs. The results of this study show that load control and energy distribution using simulation studies may be done effectively. This study used suggested algorithm to show stable frequency and controllable voltage dips. Additionally, this study contributes to a better knowledge of microgrid control.