Edge AI-Enabled Dynamic Power Factor Correction using TinyML, Blockchain and IoT for Real-Time Smart Grid Optimization and Industrial Applications
Comparative Analysis of MPC and PID Controller for Liquid Level Maintenance in a Coupled Tank System
Design and Implementation of a Smart Renewable-Powered Electric Vehicle with Intelligent Parking Integration for Sustainable Urban Mobility
Speed Control of BLDC Motor using Microcontroller
Simulation of Fuzzy Logic Based Single Phase Matrix Converter as an Universal Converter
Design and Development Of Paddy Cutter Using Solar Energy
Design Of Double-Input DC-DC Converter (DIC) Solar PV-Battery Hybrid Power System
Comparison of Harmonics, THD and Temperature Analysis of 3-Phase Induction Motor with Normal Inverter Drive and 5-Level DCMI Drive
Application of Whale Optimization Algorithm for Distribution Feeder Reconfiguration
Detection and Classification of Single Line to Ground Boundary Faults in a 138 kV Six Phase Transmission Line using Hilbert Huang Transform
The Modeling of Analogue Systems through an Object-Oriented Design Method
Circuit Design Techniques for Electromagnetic Compliance
A Technological Forecast for Growth in Solid-State Commercial Lighting using LED Devices
Testing of Analogue Design Rules Using a Digital Interface
Simulation and Transient Analysis of PWM Inverter Fed Squirrel Cage Induction Motor Drives
This work presents a comprehensive design and implementation of an AI-enabled Smart Power Factor Correction (PFC) System that integrates advanced technologies such as Machine Learning (ML), Deep Learning, IoT, Edge Computing, and Blockchain with conventional PFC hardware. The proposed system intelligently compensates reactive power and improves power factor in real time by dynamically switching capacitor banks based on load predictions and power quality analysis. At the hardware level, the system utilizes components such as Arduino Uno, ACS712 current sensor, LM358 op-amp, single-channel relays, and ceiling fan capacitors, while more advanced processing is supported through ESP8266/ESP32 modules for connectivity and Jetson Nano or Raspberry Pi for edge AI inference. The ML algorithms, trained using historical load data and power quality parameters, run either on embedded microcontrollers (TinyML) or edge devices for low-latency decision-making. Additionally, a smart capacitor bank is used to provide fine-grained control over reactive power compensation, and system logs are securely recorded through a lightweight blockchain node to ensure transparency in smart grid environments. The integrated ThingsBoard and Node-RED dashboard enables remote monitoring and real-time analytics for system adaptation and performance tracking. Simulation and hardware results demonstrate a significant improvement in power factor correction accuracy and response time compared to conventional fixed or manually switched capacitor systems. The proposed AI-driven model not only adapts to dynamic and nonlinear load conditions but also reduces over- or under-compensation through predictive switching. Comparative analysis confirms enhanced Total Harmonic Distortion (THD) reduction, power factor stabilization, and improved system resilience under varying load profiles. The integration of AI and smart technologies thus marks a promising advancement toward intelligent, autonomous, and transparent power quality enhancement in next-generation smart grids.
In process industries, liquid level control in tanks and flow regulation between interconnected tanks are fundamental yet challenging tasks due to the complexity and dynamic nature of such systems. This paper addresses the need for effective control strategies by comparing the performance of a Model Predictive Controller (MPC) with that of a conventional Proportional-Integral-Derivative (PID) controller in a coupled tank system. MPC has gained significant attention over the past two decades, particularly in the chemical, petrochemical, and metallurgical sectors, due to its ability to handle multivariable systems and incorporate system constraints. In this study, both controllers are implemented to maintain the desired liquid level, and their performances are evaluated using key time-domain metrics, including overshoot and settling time, as well as various performance indices. Simulation results demonstrate that MPC significantly outperforms the PID controller, offering superior dynamic response and robustness under varying operating conditions.
The rapid growth of urbanization and the pressing need for sustainable transportation solutions have led to a surge in innovations around electric vehicles (EVs) and smart infrastructure. This paper presents a novel and integrated prototype of a Smart Solar Electric Vehicle (EV) combined with an Intelligent Parking Management System aimed at transforming urban mobility into a cleaner, safer, and more efficient ecosystem. The Smart Solar EV utilizes photovoltaic panels to harness renewable energy, supporting an eco-friendly and zero-emission drive. It features advanced microcontroller- based control through Arduino Uno, motor driver modules, ultrasonic and temperature sensors, and wireless connectivity through Bluetooth. A real-time OLED display offers system diagnostics, including battery level, obstacle detection, and thermal conditions. The Smart Parking System is equipped with IR sensors, an LCD interface with I2C communication, and a servo-controlled automated gate, offering real-time vehicle detection, slot availability, and congestion-free operation. This dual-prototype solution not only promotes pollution-free commuting but also provides a robust foundation for smart city integration. The hardware system is designed to be compatible with various energy inputs (solar, battery, and grid), making it versatile and scalable. As a first-of-its-kind integration with low-cost yet high-efficiency components, this work stands as a benchmark in green mobility and intelligent urban planning.
Brushless DC motors (BLDC) are becoming increasingly attractive in a large number of applications due to performance advantages such as reduced size and cost, reduced torque ripples, increased torque-current ratio, low noises high efficiency, reduced maintenance and good control characteristics over a wide range in torque–speed plan and precise speed control. This paper focuses on the design and implementation of a BLDC motor speed control system using a microcontroller. The microcontroller generates Pulse Width Modulation (PWM) signals to regulate motor speed, which are supplied to the motor driver to ensure smooth operation and efficient performance. Additionally, motor protection was addressed using temperature sensors, and fire sensors were implemented for safety. This paper involves controlling a BLDC motor's speed using a potentiometer, Raspberry Pi Pico, and a motor driver. The Raspberry Pi Pico generates a Pulse Width Modulation (PWM) signal based on potentiometer input. That signal is given to the motor driver; the motor driver provides a variable voltage to the BLDC motor depending on the PWM duty cycle. The motor's speed increases/decreases based on the potentiometer's position. The temperature sensor is used for monitoring of overheating in the motor driver and motor.
The single-phase matrix converter is introduced in this paper as a general-purpose converter for fuzzy logic high- frequency step-down operation. This study introduces a matrix converter that can be utilized as a rectifier, chopper, inverter, or cyclo-converter. Therefore, fewer new or extra converters will be required. The proposed topology was implemented using the fuzzy logic methodology. This work confirms the four conversion procedures of a single-phase matrix converter alone: AC-DC, DC-DC, DC-AC, and AC-AC. The results of the filter and the four converters are shown in this study.
