Renewable energy-based smart grids are famous nowadays due to their high intellectual properties. The world is starting new inventions in renewable energy-based electrical power generation systems to reduce global warming. However, a single renewable energy source cannot maintain a proper energy management system and reliability of power towards loads. Hence, integrating two or more systems is very important and can form a smart grid with an appropriate energy management system. Effective energy management system for a 4-wire 1-MW smart grid system is proposed in this paper. The system is composed of three solar plants and three wind farms with a battery bank. The battery energy management system can operate the complete system as a smart grid with the proper design of the controllers. The maximum power points of PV plants are tracked using a hybrid algorithm that merges the merits of Modified Invasive Weed Optimization and Perturb and Observe (P&O). Thus, the maximum power is obtained under partial shading conditions. The P&O algorithm is also developed to track the maximum power of wind farms. All the loads and generation units are connected in a ring-configuration distribution with a centralized battery energy management system. The loads are selected to be unbalanced, nonlinear and reactive to simulate practical cases. TS-Fuzzy based common inverter controller is implemented to maintain acceptable power quality, which is linked to the battery. The proposed inverter controller can work as a reactive power compensator, active power filter, voltage regulator under unbalanced load, and power balancing device between generation and load. Extensive Hardware-in-Loop (HIL) results are presented to validate the effectiveness of the proposed system.

This article shows that when an accident occurs, the time it takes for an emergency medical facility to be established and put into operation has a significant influence on the survival of the victim. Reducing accident scene time is considered by medical professionals to reduce mortality. Emergency responders can be alerted to disasters using the Raspberry Pi-based accident identification system. This helps to shorten response times. The vibration sensor detects the error and then sends the prepared message to the right person. It is important to know what happened and who was involved in an accident in order to send appropriate information to emergency responders. It is possible to get precise latitude and longitude positions for satellites if GPS is first used in this way. In order for the GSM device to start tracking the vehicle, need to send a message to it. The Raspberry Pi controller's vibration sensor can also be used to identify faults.

The purpose of smart parking systems is to optimize the use of parking spaces in urban areas by combining sensors, cameras, and software. The systems detect parking spots automatically and notify users in real-time, instead of requiring manual searches for parking.They contribute to the reduction of traffic congestion, fuel consumption, and environmental pollution by reducing time spent searching for parking. This paper proposes a vision-based smart parking system that utilizes deep learning techniques for real-time detection and monitoring of parking space occupancy. The system employs YOLOv7 for object detection, integrated with a Flask-based web interface and OpenCV for image preprocessing. The SQLite database makes it possible for a responsive web application to deliver timely, accurate information with low latency to users. The experimental results presented in this paper indicate that the system achieved an overall average detection accuracy of 94.23% across diverse environmental conditions, which reflects the project’s scalability and dependability. Furthermore, our approach offers a low-cost, easy to implement solution with minimal dependence on hardware-based infrastructure that can be applied in a wide range of land use in urban contexts. Furthermore, the project aligns with securing critical infrastructure in next generation networks, promoting urban mobility, enhancing intelligent transportation systems, and ensuring resilience of smart urban services that are resource-efficient and accessible. This AI-enhanced framework presents an opportunity to protect and optimize critical urban infrastructure using AI-based technology, while also using machine learning in its real-time decisions.

When an accident occurs, the time it takes for an emergency medical facility to be established and operational has a significant impact on a victim’s survival. Reduce the time it takes for an accident scene to be examined by a medical professional to reduce the death rate. Emergency responders can be alerted to a disaster by using a Raspberry Pi-based accident identification system. This helps to shorten response times. Vibration sensors detect errors and then send a prepared message to the right people. It’s important to know what happened and who was involved in an accident in order to send the appropriate information to emergency responders. It is possible to get accurate longitude and latitude positions for satellites if the first GPS is used in this manner. To get the GSM device to start following the car, you must send it a message. The Raspberry Pi controller’s vibration sensor can also be used to identify the error. A pre-programmed emergency server receives every GSM emergency call, no matter where the caller is located.

Because of their high efficiency, better starting torque, and minimal electrical noise, permanent magnet brushless DC (PMBLDC) motors are frequently used in a variety of industrial applications. The speed of PMBLDC motors is controlled by a variety of controllers. In this study, P, PI, and PID controllers are used to compare the speed control of a permanent magnet brushless DC motor drive powered by solar PV arrays. The Perturb & Observe (P&O) technique is used to find the MPPT. The drive system’s simulation results for various operation modes, such as constant and variable load circumstances, are examined and evaluated. When using a PID controller instead of a P or PI controller, the drive performs better at controlling speed. The MATLAB/Simulink software was used to model, control, and simulate the permanent magnet brushless DC motor drive. The whole drive system is put into operation with the help of the dSPACE MicroLab Box 1202.