Journal of Electrical Engineering and Automation
Autonomous and Collaborative Robots for Energy Systems
The global change of sustainable energy has increased the demand for robotic systems that can work independently from humans to work together safely with their connections through the energy systems (e.g. energy generation plants, transmission lines, renewable energy fields, substations and underground cable networks). Now-a-days, installing robots into the energy systems creates very hazardous and complex environments with time sensitive constraints in which traditional automation methods will not work. The use of autonomous robots (robots that can operate without human interaction) and collaborative robots (robots that can operate with humans) has the potential to deliver safe, reliable and efficient operation of robots in these work environments using technologies for perception (how a robot sees its environment), learning based decision making (make decisions using the information it has learned), distributed control (how robots operate as a distributed system) and collaborate with humans (work with human operators). This review presents a systematic, synthesis of recent advancements in collaborative and autonomous robotics with energy related applications. The review of technology advancements and deployment constraints will use 26 studies published in the major scientific software databases (IEEE Xplore, Scopus, Web of science, ScienceDirect, SpringerLink, & MDPI repositories) from 2024 - 2025. This review focuses on enabling technologies of cognitive and explainable autonomy, navigation using reinforcement learning, multi-robot coordinate, digital twin integration, fault tolerant control systems, energy aware task orchestration and Human-centred interactions through industry 5.0. This study has been categorized based on the type of contribution (research vs. development), Deployment context and technology advancement. It shows that individual capability areas such as navigation intelligence, collaborative interaction and multi-robot coordination continue to progress significantly. It identifies three primary challenges for the continuous improvement of autonomous robotic systems operation and additional research areas like the completeness & reliability of system operation under severe environmental conditions, Energy aware mission planning and scalable coordination of heterogeneous teams of robots with effective human-in-the-loop supervision. This review contains the identification of the most significant research gaps, an outline of how to develop robust, energy aware and Human-centred robotic systems that will facilitate energy infrastructure of the future.
Copyright (c) 2026 Journal of Electrical Engineering and Automation
Design and Development of Intrinsically Safe Lightning Arrester System for Firework Industries
The fireworks production process is highly risky for lightning-related accidents due to the presence of explosive substances and exposure to severe weather conditions. The existing fire protection system depends on ineffective surge and grounding management techniques without any automated emergency service in real-time. This article discusses the design and implementation of a fundamentally safe, affordable, IoT-enabled lightning protection and early alert system developed for the fireworks industry. This model combines environmental sensing, artificial lightning modeling, electrical fault monitoring, fire detection and automated fire control. Weather conditions such as temperature, humidity, rainfall, noise levels and water levels are continually monitored to detect the irregular variations caused by simulated lightning strikes using voltage and current sensors. The experimental results show fire detection (~0.3s), automated fire protection activation within 1.5s, reliable cloud data updates every 15s, and WhatsApp alert notifications sent within 6–8s. This system operated continuously for 8 hours with stable performance and achieved AI-based weather risk prediction accuracy of approximately 82%. These results confirm that the proposed work is an effective smart safety measure for improving lightning-related fire protection in fireworks manufacturing industries.
Smart Vehicle-to-Vehicle Wireless Charging System with IoT-Based Location Tracking
The increased use of Electric Vehicles (EVs) provides smart charging solutions to prevent limitations and dependence on fixed charging stations. This research work presents a Smart Vehicle-to-Vehicle (V2V) wireless charging system with IoT based location tracking to allow direct energy transmission between vehicles. This system integrates a dual-switch method to control the energy transfer. One switch will initiate a charging request and the other switch will allow or reject the request. The Wireless Power Transfer (WPT) method is used for efficient power transmission and an ESP32 microcontroller allows real-time data collection and system management. The main hardware components used in this research are a voltage and current sensor, a temperature sensor, and a GPS module for monitoring energy flow, safety and tracking the vehicle’s location. This work also improves the charging range, reduces dependency and allows decentralized energy transmission. This study provides a cost effective solution to improve EV charging performance and access by combining IoT technology with wireless charging.
Autonomous and Collaborative Robots for Energy Systems: State-of-the-art Review
Energy systems are becoming larger, more complicated and distributed system. Such environments make manual operation and investigation based on unsafe, slow and inefficient operations. The independent and cooperative robots have been realized as viable systems in energy infrastructures to monitor, maintain and assist in operating the facilities. Recent research indicates a gradual increase in automated, energy-sensitive work and combined task performance of robots. Simultaneously, energy systems have severe limitations in safety, reliability and power efficiency. According to research explained in recent surveys, energy-efficient movement planning, smart power controlling and collaborative working among robots in energy-based tasks are most important. This paper reviews autonomous and collaborative robots in energy systems. It considers energy infrastructures as robotic workplaces, discusses autonomous and collaborative robots and reviews energy consumption modelling and optimization plans. The system integration and real-life implementations are also discussed. Finally, the main problems and future study directions are described. This survey aims to provide a brief and methodical source of data for researchers and engineers working on robotics and energy systems.
Future of Dynamic Electric Vehicle Challenges and Solutions
Dynamic Electric Vehicles are an advanced, sustainable, alternative to traditional Electric Vehicle technology that reduces the major issues associated with traditional EV technology (i.e. range anxiety, long recharging times and the requirement of high-capacity batteries). The application of Dynamic Wireless Charging (DWC) systems and new smart energy systems will improve the performance and accelerate the adoption of EVs. The methodology for this study performs a longitudinal review of the literature addressing DWC systems and included a number of case study examples from the various previous works. DWC systems based on either inductive or resonant power transfer, V2I (vehicle-to-infrastructure) communication, integration with smart grid, improved battery technology and dynamic load management systems were evaluated. The results indicate that dynamic charging systems are technologically capable and will provide improved performance compared to traditional EVs based on the case studies that used from Europe, North America and China. The results of this research show that charging EVs will decrease the time that EVs spend while they are in movement. It will remove the range anxiety and reduce the amount of battery power that EVs require.
Robust Fault Detection and Classification in Power Systems via Physics-Informed and Data-Driven Learning
Electrical faults in power transmission systems can severely affect grid stability, equipment safety, and operational reliability. Traditional protection schemes, particularly distance relays, depend on apparent impedance computation that changes with error, creating a risk of misclassification. The results from relay overreach, underreach, or complete maloperation due to CT/PT saturation lead to developing problems in high impedance situations. These limitations highlight the need for adaptive, data-driven alternatives. This paper proposes an intelligent fault detection and classification model based on supervised machine learning techniques that overcome these challenges. The system’s robustness was validated under different training sizes and Gaussian noise levels, demonstrating consistent accuracy and generalization across diverse learning conditions. The presented approaches learn the complex nonlinear mapping between three-phase voltage/current patterns and the associated fault type, without assuming fixed impedance paths like traditional protection schemes. This method extracts a high set of derived features to represent the distinguishing characteristics of six fault categories by utilizing line voltages and currents. Different models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests, XGBoost, Long Short-Term Memory (LSTM), and Physics-Informed Neural Networks (PINN) are developed and processed on SMOTE-balanced datasets. These models classify the errors without fixed thresholds or fault loop assumptions, improving sensitivity and robustness. The supervised machine learning approaches bridge the gap between traditional impedance-based protection and smart, scalable data-driven grid analytics that are implemented into a wide area monitoring and control system. The PINN achieved the highest fault detection accuracy of 99.86% while sustaining 99.79% multiclass classification accuracy on the clean dataset. The PINN maintains high accuracy under 2–5% noise and 1–60% training data, providing millisecond-level inference by embedding power system equations, which enables accurate real-time protection by understanding the missing simple data-driven parameters.
Copyright (c) 2025 Journal of Electrical Engineering and Automation
Multi-Purpose Voice Control Patient Assistance Robot
Timely and regular treatment of the physically disabled, bedridden, and elderly is a mandated health requirement. A multi-purpose voice-controlled patient care robot (VCPAR) designed and implemented for on-demand drug, food, and liquid delivery in natural language is presented in this paper. In contrast to conventional button/joystick-based or human-attention-based robotic systems, the approach utilizes Arduino-based control, Bluetooth communication, and speech recognition to facilitate completely hands-free usage. IR sensor-based obstacle sensing and accurate actuating devices ensure safe transport and efficient dispensing of goods. Another of the research's novel contributions is its low-cost, locally manufactured, and multimodal platform for use in resource-constrained settings where advanced healthcare robots are cumbersome. Experimental findings demonstrate agreement with voice instructions from the patient at a distance of 10 m or less from Bluetooth, precise delivery of objects with minimal latency, and greater user control compared to current semi-automatic systems. The project aims to explore the potential of low-cost voice-controlled robots for maximum patient autonomy, caregivers' load reduction, and new opportunities for smart healthcare support systems. Intellectually enabled assistive technology has been in high demand due to the increasing numbers of elderly, bedridden, and physically disabled patients who otherwise need to remain constantly supervised and receive timely interventions.
Voice Controlled Application using ESP32
The ESP32 microcontroller is used in this project to develop voice applications for device automation. The ESP32's built-in Bluetooth and Wi-Fi capabilities make it the perfect platform for Internet of Things (IoT) applications, where it can perform voice-activated tasks both online and offline. An external microphone records the voice commands, which the system stores before being interpreted by edge-based speech recognition software like TensorFlow Lite and Vosk, or cloud-based services like Google Assistant or Amazon Alexa. Relays or the ESP32's GPIO pins are used to translate the voice commands into control signals that can be used to operate lights, fans, and other devices. The system can function in any network condition thanks to its support for both offline and cloud computing. The application showcases the versatility of the ESP32 in real-time voice-activated home systems, which improve accessibility and user convenience through voice control.
Smart Thermal Regulation for Patient Care using Body Temperature and Room Temperature Monitoring
A speed control fan system has been designed to enhance the comfort level of patients and conserve energy. It is extremely helpful for those patients who may not be able to shift their positions, as it continues to monitor the body temperature of the patient along with the room temperature continuously. The fan itself automatically changes speed using sensors and PWM technology, leaving the user with no work to manually do so. The fan speed is modulated by temperature in a linear and accurate way, as determined by testing with Tinkercad simulations. A manual override through a potentiometer can be used if needed, and an LCD display using an I2C adapter provides for the ability to monitor system activity. Other enhancements to improve the system further involve more advanced control algorithms, integration with wearable technology for enhanced temperature sensing, and remote control and monitoring.
Environmental Air Pollution and Water Quality Systems in Educational Institutions
An intelligent environmental monitoring system receives real-time conditions through continuous interpretation of several environmental parameters to provide dependable results alongside ongoing updates. The data communication through IoT connectivity creates a smooth transmission to central platforms which enables remote observation along with preventive decision-making to boost health and environmental sustainability. The system operates over extended periods by using automated analytics that track environmental changes with data-driven evaluation methods. It provides an accessible platform that helps users track essential parameters effectively, supporting public security and healthcare initiatives. The system enables environmental management alongside public health due to its feature set, which includes early alerts, quick responses, and strategic decision-making abilities. It requires the integration of machine learning and improved data analytics to advance its prediction functions.
The stand-alone creatinine test kit design is home care monitoring technology, i.e., patients at risk of kidney injury or chronic kidney disease (CKD). The new kit enables individuals to self-calculate their own creatinine levels better and more precisely without necessarily utilizing the services of well-trained professional health care providers. The primary intention of this kit is to reduce the need for professional medical care to the utmost level, thus emulating an automated process. The test kit is provided with a simple, easy-to-handle system, including a urine sample collection device and colorimetric assay for the determination of creatinine. It can provide results in a few minutes with instant feedback on kidney function. The self-testing creatinine kit aims to ensure patient autonomy and allow for the early detection of kidney disorders, thus facilitating early medical care. The kit has been prepared for use by common patients who are far from hospitals or do not have easy access to hospitals.
Artifact Sentinel: A Real-Time IoT System for Artifact Safety and Conservation
As everyone knows, museums are houses for priceless artifacts that need to be preserved at all costs. It is important to note that these artifacts need to be stored in a suitable environment for preservation. The proposed "Deformation Evidence Guard" is an Internet of Things device that provides protection for deformation evidence and ensures that its environmental norms are maintained. Various sensors in this device work together to ensure that desired results are obtained. An alarm beeps, and an immediate alert is issued when movement is detected near an artifact, and an ESP takes a picture of a 32-came intruder. Technology also enables the sensor to continuously monitor temperature and humidity levels, presenting results on an LED display to help preserve as many artifacts as possible. This simple, automated device increases museum protection by reducing the possibility of theft and deterioration through real-time monitoring and environmental tracking. The proposed model using high energy efficiency, the ability to monitor surroundings, motion sensing, and an attached camera, as well as features such as real-time reactions, consistent monitoring, and detailed tracking, making it a novel approach compared to existing.
Smart IoT Energy Metering System with Real-Time Theft Detection and Prevention
This article describes the design and development of a real-time, IoT-based smart energy meter for detecting electricity theft using a low-cost Arduino Uno, ESP8266 Wi-Fi module, and Blynk IoT platform, relying on threshold-based detection methods. In contrast to conventional energy meters that do not have tampering detection features, the designed system constantly checks for energy consumption using current sensors and monitors data to detect unauthorized power consumption. The uniqueness of this research is its cost-effective dual-load configuration that mimics both legal and illegal utilization scenarios, allowing real-time detection and instant notification through cloud-based dashboards. Experimental validation proves effective bypass scenario detection and distant notifications with negligible latency, making it ideal for smart grid integration in developing countries. The system improves energy accountability, enables remote monitoring, and offers a scalable solution to help fight electricity theft effectively.
Design and Implementation of CMOS K-Band Phase Shifter using 90nm
We suggested designing a 24 GHz K band CMOS phase shifter using 90nm technology in this paper to satisfy the need for integrated and high-performance components for wireless system development. For the best phase shift and the least amount of insertion loss, the phase shifter operates between 18 and 27 GHz using a combination of lumped and distributed components. Important considerations are phase selectivity, linearity, and impedance matching. Theory and experiments demonstrate that it has a 180° range of phase shift and is utilized in high-frequency applications. The study demonstrates how 90nm CMOS technology can be used to improve wireless communication.
Complementary Metal Oxide Semiconductor (CMOS)
Automatic Smart Defense based Surveillance Security System using IoT
The Automatic Smart Defense-Based Surveillance Security System is an advanced system proposed in this study to improve real-time security surveillance and eliminate danger. Traditional surveillance technology relies heavily on human interaction which, leads to inefficiencies, delays, and mistakes made by humans. To overcome these limitations, the proposed system uses radar technology, AI, and IoT to detect, monitor, and remove potential threats such as drone attacks and illegal activities. Automating the surveillance will reduce false alarms, minimize dependency on human involvement, and provide continuous monitoring with quick reactions. High-security areas, including military camps, border patrol zones, airports, and other important buildings are the most suitable for this system to enhance security.
Long-time and effective control of water temperature is critical for both commercial and domestic use. This project designs a new dual-application water temperature control system for drinking and bathing water use by integrating thermoelectric cooling and immersion heating technology. The system employs a Peltier coil water flow block in cooling and an immersion water heater in heating. A fan-based heat dissipation scheme enhances thermal efficiency to ensure optimal system performance. An ESP-based microcontroller offers automated water level control and real-time monitoring through tank refilling control and monitoring of water levels. A temperature sensor DS18B20, with its precise thermal measurements within the storage tank, offers precise feedback control. The switching process of heating and cooling is maintained through a relay-based switching mechanism, ensuring that maximum energy efficiency is retained with optimal temperature levels. This system provides an innovative, energy-efficient, and affordable means of regulating the temperature of water in domestic and business premises. Monitoring enabled through IoT further increases user-friendliness by allowing real-time monitoring of temperatures and automated regulation of the system. The suggested method increases convenience for users and promotes eco-friendly use of resources by offering a flexible and scalable solution to existing issues of water heating and cooling.
Decentralized Control Strategy for Voltage Regulation in Islanded DC Microgrids
Every distributed energy resource (DER), including battery systems and solar panels, has local controllers that respond to variations in frequency and voltage to modify power generation in order to maintain voltage stability. In this context, traditional droop control is commonly used to control variable states of charge (SoC) in battery energy storage systems (BESS) and to enable decentralized control for load sharing among distributed generator (DG) units. While grid integration provides increased flexibility and resilience, decentralizing energy management allows the system to optimize local energy resources independently, creating a resilient and flexible system that can react to changing grid conditions.
Battery Energy Storage Systems (BESS) Units
Design of Hand Assistive Device for Autistic Children
Autism spectrum disorder (ASD) is a developmental disability related to neurological variations, affecting children's social communication, behavior, learning, and attention. These children often struggle with movement, as they require external support to move their hands and legs. A hand assistive device has been designed to help these children, providing them with movement exercises and physiotherapy. This device also uses EMG (electromyography) sensors to diagnose neuromuscular activities in autistic children. This device is essential for their overall well-being and development.
Lithium-ion batteries are an important component in electric vehicles (EVs), and ensuring their optimal performance is vital for the longevity and efficiency of the vehicle. Efficient testing of battery cells plays a major role in verifying their performance. Traditionally, a single-cell testing unit is used for batch processing during battery pack assembly. However, this method is time-consuming and lacks scalability, which limits productivity. To address this, the research proposes a multicell testing approach that concurrently estimates the state of charge (SOC) and state of health (SOH) of multiple battery cells in parallel. By doing so, the approach significantly reduces testing time and improves efficiency. The dual filter concept is incorporated to categorize cells based on their performance, ensuring only high- quality cells are selected for inclusion in the battery pack. Furthermore, a custom Temporal Convolutional Network (TCN) model, achieving an accuracy of 89%, is employed to accurately estimate SOC and SOH. In addition, a predictive battery temperature forecasting model is introduced to forecast the temperature of the battery cells over the next three days, which aids in proactive temperature management and prevents potential degradation. Overall, the proposed approach enhances battery testing productivity and ensures higher accuracy in SOC and SOH estimation, contributing to the development of more reliable and efficient EV batteries.
Optimized Dual Loop Control in PV – based LVDC Microgrid With Hybrid Energy Storage for Enhanced Power Management
This study proposes a power management plan for an LVDC (Low-Voltage Direct Current) microgrid that is linked with solar energy and connected to a HESS (Hybrid Energy Storage System) that consists of a supercapacitor and battery. This study analyses the microgrid's performance with a focus on integrating hybrid energy storage devices to manage the unpredictable nature of renewable energy resources and load demands. The primary goals are creating a DC link voltage controller, building the battery and supercapacitor current controller, and activating a solar power generating unit using the Perturb and Observe MPPT algorithm. Additionally, a dual loop control strategy is created for the HESS. The power management method controls DC voltage and maintains power balance.
Improving Lithium-ion Battery Longevity through Predictive Thermal Management
Lithium-ion batteries are vital in various storage applications like electric vehicle, renewable energy system and industrial applications. One of the major drawbacks with lithium- ion batteries are overheating. A cost-effective thermal management system is proposed where predictive analysis is employed to reduce thermal stress, thereby increasing reliability and longevity of batteries. Linear regression techniques is employed to forecast temperature fluctuation and activate cooling system when temperature of the lithium-ion battery reaches unsafe limit. The proposed methodology is developed and validated by a low-cost hardware.
Mathematical Modelling and Simulation of Voltage Source Converter
This research provides the mathematical modeling and simulation of a Voltage Source Converter (VSC) employed in advanced power systems for effective DC to AC voltage conversion. The research investigates the dynamics of VSCs, emphasizing important components like Pulse Width Modulation (PWM), dq current control, and PI controllers, coupled with an LCL filter for harmonic suppression. The control strategies, voltage and current regulation are proven using simulation responses with stable and precise output voltage and current. The results confirm the significance of robust control algorithms in obtaining optimum system performance and lay a groundwork for further study.
Enhancing Safe and Precise Maneuvering in Autonomous Electric Vehicle
This research deals with the design of an autonomous vehicle navigation system based on a master-slave computational paradigm, combining Raspberry Pi 4 and Arduino UNO for visual perception in real-time, decision-making, and actuation. The Raspberry Pi 4, supported by a Pi Camera module, performs lane detection and near-object identification through image processing techniques, and then sends over extracted data through serial communication to Arduino UNO. Contrary to traditional autonomous driving systems that are based largely on monolithic processing architectures, this two-part control paradigm maximally splits computational loads, thus maximizing real-time responsiveness and efficiency of computation. The approach coordinates an optimal interaction between image-based navigation and multi-sensor fusion to guarantee improved trajectory planning and obstacle avoidance. In addition, the servo-actuated ultrasonic module provides an extended spatial detection range over traditional static-sensor deployments, thus allowing for better environmental adaptability, especially in small or dynamically changing environments.
Single Switch Flyback PFC Converter for Optimal Efficiency in BLDC Motor Drives
This research presents the design and implementation of a single-switch flyback PFC (Power Factor Correction) converter for Brushless DC (BLDC) motor applications. By operating in Discontinuous Conduction Mode (DCM), the converter achieves a power factor exceeding 0.9, improving efficiency and reducing energy losses. The converter provides electrical isolation, ensuring safety while integrating power conversion and power factor correction in a single stage. A lossless snubber circuit is included to mitigate voltage spikes and recycle leakage energy, further enhancing efficiency. Simulation results show the converter delivers 48V and 1A with high efficiency across varying loads. Its compact design reduces component count and manufacturing costs, making it ideal for space-efficient solutions in modern electrical systems. This work demonstrates the potential of single-switch flyback PFC converters in optimizing BLDC motor performance and ensuring reliable power quality.
Development and Realization of a Multi-Rate FIR Filter Utilizing Distributed Arithmetic on FPGA
This research presents an efficient development of a multi-rate Finite Impulse Response (FIR) filter utilizing Distributed Arithmetic (DA) for digital signal processing (DSP) applications. The motivation behind this work lies in the increasing demand for high-performance filters in various multimedia and communication systems, where low power consumption and area-efficient designs are critical. The proposed FIR filter utilizes DA to minimize the resource requirements typically associated with conventional filter implementations while achieving comparable or improved performance metrics. A Look-Up Table (LUT) based approach is employed to compute filter outputs, allowing for rapid and efficient processing of input samples. Simulation results demonstrate that the multi-rate FIR filter significantly minimizes the computational complexity and enhances speed, Optimizing it for real-time performance in embedded systems. The findings emphasize the significance of integrating Distributed Arithmetic into FIR filter designs., paving the way for more advanced signal-processing solutions in VLSI architectures. This proposed design is implemented using Vivado version 2023 in Zynq UltraScale + MPSoCs family.
Agro Guard Edge AI - Development of Sustainable IoT Framework for Wildlife Intrusion Detection
The rising global population and increasing food demand have placed immense pressure on agriculture, particularly in regions like the Marudhamalai foothills in Coimbatore district, where farmers face frequent crop damage caused by wildlife intrusions, such as wild boars and deer. To address these challenges, a wildlife intrusion detection system has been developed to safeguard crops, enhance agricultural productivity, and enable coexistence with wildlife. The system combines a laser detection setup with an AI-CAM that employs lightweight deep learning algorithms for real-time animal detection and classification. This system also ensures efficient animal deterrence and real-time monitoring for farmers, enabling them to assess the situation with the assistance of an intelligent rover built using IoT. The detection system consists of a processor, Light Dependent Resistor module, laser diode, and buzzer are used to detect intrusions with precision and provide an immediate response. The system includes an ESP32-CAM to monitor and deter animals effectively. It features live image processing through an OLED display and a USB-to-TTL adapter, ensuring reliable performance with minimal power consumption. By integrating IoT technology and advanced monitoring systems, farmers receive real-time updates and can remotely control a rover for necessary interventions. The system achieves 96.3% accuracy in real-time animal detection using an ESP32-CAM and a YOLO v8 model, with results displayed on an OLED screen and bounding boxes for classification.
RoboSpy: Autonomous Night Vision Surveillance Robot with Spying Camera for War Field Operations
RoboSpy is an autonomous surveillance robot designed for war field operations, equipped with night vision capabilities and a spying camera. Its primary goal is to provide real-time intelligence, enhance situational awareness, and reduce risks to soldiers by conducting reconnaissance in hazardous conditions. RoboSpy integrates advanced sensors, hardware, and software to navigate complex environments, detect obstacles, and transmit critical data to remote operators. The system architecture features a Raspberry Pi as the processing unit, ultrasonic sensors to detect objects, and a high-resolution night vision camera for real-time video capture. The software utilizes an intelligent HAAR Cascade classifier algorithm for object detection, path planning algorithms for navigation, and enhanced image processing techniques to capture the moving image. The RoboSpy provides real-time support for security forces to monitor the movement of intruders in the restricted area. This research discusses the design, implementation, and potential military applications of RoboSpy, emphasizing its role in reducing human intervention during high-risk reconnaissance missions.
Automated Inventory Tracking and Alert System
The research is on inventory management systems, focusing on IoT-based solutions, RFID technology, and machine learning applications. The study examines various systems, including automated inventory tracking, real-time monitoring, and predictive analytics for demand forecasting. The survey highlights the benefits of these systems, such as improved inventory accuracy, reduced stockouts, and enhanced customer satisfaction. However, it also discusses challenges and limitations, including dependency on network connectivity, initial setup costs, and data accuracy concerns. This survey provides a comprehensive overview of the current state of inventory management systems, identifying opportunities for future research and development.
An Efficient Analysis of Cloud-based Energy Management System for Secure Data Transmission
The traditional method of manual electricity meter reading has limitations, including human errors, inefficiency, and resource intensity. This research proposes a Smart GSM-Based Automated Power Meter Reader System to streamline power consumption monitoring. The system utilizes a GSM module integrated with a digital energy meter to provide real-time, remote power consumption data to utility providers and consumers. This approach reduces manual intervention, enhances accuracy, and promotes energy management. Finally, a user-friendly interface is developed to visualize real-time energy statistics through interactive graphs and tables, enabling users to track consumption trends and make informed decisions.
Copyright (c) 2024 Journal of Electrical Engineering and Automation
Remote Temperature Monitoring and Control System Using SCADA for Large Industrial Environment
In large-scale food companies to ensure product quality and protection, precise temperature control must be maintained. This research presents the design and implementation of a SCADA (Supervisory Control and Data Acquisition) system integrated with a PID (Proportional-Integral-Derivative) control strategy for effective temperature regulation. The system offers operators a comprehensive Human-Machine Interface (HMI) for real-time visualization and control, processes temperature data through a PID controller to maintain a setpoint of 85°C, and continually monitors temperature data in real-time. The simulation results demonstrate that despite outside disruptions, the system can maintain the required temperature within close tolerances, assuring ideal process conditions. Large-scale industrial applications of SCADA systems are emphasized by their function in advanced data analysis, supervisory control, alarm management, and data collecting.