Electrical Engineering
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- ItemIMPACT OF ELECTRICAL VEHICLES CHARGING ON THE MAXIMUM DEMAND OF THE POWER GRID AND THE POLICES FOLLOWED TO CONFRONT IT(2025-09-18) Talal Amjad Talal DweikatAbstract With the increasing number of electric vehicles and increased government adoption, challenges are mounting for electrical grids around the world. One of the most significant challenges is the rising demand for electrical grids and its impact on the maximum load, especially during peak hours. This research investigates the impact of the widespread deployment of electric vehicles on the grid's daily load curve, and the strategies that can be used to mitigate the stress on the grid. The user behavior of electric vehicles and their charging methods were studied through a questionnaire. The results obtained from the analysis of user data enable the identification of several important elements in this study. Data such as the prevalence of electric vehicles, the capacity of the chargers used, the average battery capacity available in the market, and the behavior of the electric charger user during usage times are all elements that help in studying the impact of the increase in electric vehicles and providing appropriate recommendations for each network separately. Using the results obtained from the user behavior study and drawing a daily load curve for new loads, it is possible to identify areas of high loads and how they are distributed across the network spatially and temporally. Determining the daily load curve for vehicle charging is the key point in providing recommendations for the use of network management strategies. To address these challenges, this study examines demand-side management (DSM) strategies such as demand response, load shifting, smart grid, and smart charging. Implementing DSM strategies enables the grid to handle increased electrical loads while maintaining network stability and quality. The study includes specific recommendations for increasing grid reliability and addressing the challenges of increased loads, including the integration of storage systems into the grid, the use of mechanisms such as V2G, and smart charging policies for electric vehicles. These strategies improve electricity service while reducing network stress and decreasing network development costs. Future work will focus on the use of more accurate methods for collecting user information, promoting the use of smart chargers connected to the electricity grid and its operators, and enhancing the policies used in this area. Keyword: Electric Vehicles (EV); Peak Demand; V2G; Consumer Behavior.
- ItemVirtual Network Operations Center (VNOC)(2025) Ranin Sharawneh; Roua Daghameen; Laila OmerThis report explores the concept of a Virtual Network Operations Center (VNOC) within the metaverse, aiming to revolutionize network management and monitoring. Building traditional Network Operations Centers (NOCs), a VNOC leverages virtual environments to enhance real-time monitoring, incident management, and network optimization. The report discusses the integration of virtual reality technologies with existing NOC functionalities, such as 24/7 monitoring, security monitoring, and network performance analysis. It outlines how a VNOC can improve operational efficiency, enhance security, and provide immersive experiences for network professionals.
- ItemFAULT DETECTION IN TRANSMISSION LINES USING ARTIFICIAL INTELLIGENCE(2025) Sameer Othman; Abdelkareem Kukhun; Khaled AzaizehMaintaining continuous power is very important for any modern infrastructure. Still, power transmission lines are likely to develop faults as a result of weather conditions, aged equipment, or contact with humans or animals. According to studies, traditional approaches to finding faults are not very accurate and are delayed. It presents a low-cost approach using Artificial Intelligence (AI) to locate and identify problems in a small-scale transmission line. The system gathers data using ACS712 and ZMPT101B sensors on Arduino and Raspberry Pi boards, and then this data is processed by a neural network. It also quickly spots an issue in the power grid and pinpoints the specific zone that has a fault. It is vital to keep power lines operational, but they often experience faults. Modern grids make it difficult for traditional ways of detecting faults. The paper outlines how a low-cost Artificial Intelligence system was built with Arduino and Raspberry Pi, along with sensors (ACS712 and ZMPT101B), to spot and mark problems on a laboratory electricity line model as they happen. The ANN was trained by using data that came from experiments with simulated normal and faulty conditions in eight zones. The system performed well by correctly locating every fault and its exact area when tested in real time. The approach allows for AI to be deployed easily on edge devices, thus helping to link the analysis done in simulations with practical applications in the real world. The process consists of collecting data with Arduino, training and predicting with Python, and identifying faults right away with the help of digital output signals.
- Itemfault detection in teansmision line using Ai(2025) abdalkarim kukhun; sameer othman; khaled azazizehThe expanding dependence on renewable energy sources has highlighted the require for productive fault detection systems in electrical networks This study aims to plan a device based an AI- for detecting faults in power transmission lines.
- ItemControl of Active and Reactive Power on a Single-Phase Grid-Tie Inverter(2025) Eng. Mahmoud Tartir; Eng. Osama Dawoud; Eng. Malak AmerAs the global demand for electricity continues to rise, Photovoltaic (PV) power integrated into the utility grid is becoming increasingly prominent. Solid-state inverters have proven to be a key technology in enabling the connection of PV systems to the grid. Typically, 2-level inverters are used to convert the DC output from the PV system into AC power for the utility. These inverters are commonly managed using Hysteresis control to regulate the flow of real and reactive power from the PV to the grid. In this project, new control techniques will be tested to control the real and reactive power generated by the inverter. One of these techniques is using the Repetitive Controller besides the PI controller to minimize any error in tracking the sinusoidal reference input. The other technique is the using of a resonant controller to achieve the same objective, as demonstrated in Figure 1. The system will first be modeled in MATLAB Simulink, followed by hardware implementation. The hardware setup will include using a microcontroller, current sensor, voltage sensors, a transformer, and building H-Bridge using IGBTs and its associated gate driver’s circuits. Additionally, all the controllers in addition of the generation of the PWM signals will be implemented in the microcontroller to regulate the injection of real and reactive power into the grid. This project distinguishes itself from previous ones through its novel control approach, the design of the H-Bridge, and the practical implementation, which was not achieved in earlier projects with previous students. Figure 1 highlights the proposed system configuration and control strategy. Figure 1 Single Phase Grid Tied Inverter With Active And Reactive Power