Journal of Informatics Electrical and Electronics Engineering (JIEEE)

An Intelligent Particle Filter with Neural Network for Fault Location and Classification in Microgrid

Archana — 2025-04-25

Microgrid concept is initiated due to increasing involvement of distributed generation resources with the utility grid. Microgrid provide reliable and sustainable power but the protection of microgrid become challenging due to bidirectional power flow, dual mode of operation (grid connected and islanded mode). Faults in the microgrid reduce its stability and efficiency. Identification, classification, and location of faults are crit-ical for rapid restoration and microgrid protection. This research proposes a neural network-based intelligent particle filter for microgrid fault detection and classifica-tion. Even with low fault current, which is typical of inverter-based DGs, the suggest-ed method seeks to precisely identify fault kinds, locations, and directions. The fea-tures are extracted from data using S-Transform, then extracted features are esti-mated using particle filter. A neural network is then used for classification and finali-zation of location. The proposed scheme provides extremely precise fault detection, ensuring that the classification and location of the fault are promptly identified for effective protection and service restoration.

Smart Home, It's Vulnerability Assessment Through Penetration Testing

Kranthi Kondru — 2025-04-25

Smart home systems, driven by IoT technologies, offer automation and remote control of household devices but also introduce significant security risks. This project develops a smart home prototype using Arduino Uno, ESP32, and relays to simulate common automation features. The goal is to assess system vulnerabilities through penetration testing techniques. Various security weaknesses were identified using Kali Linux tools like Nmap, Wireshark, and Metasploit, including insecure communication and poor authentication. The study highlights the importance of proactive testing and proposes mitigation strategies to enhance smart home security. This research emphasizes the need for integrating cybersecurity practices in smart home development to prevent potential threats and ensure a secure IoT environment.

Automatic Solar Battery Charging System with Grid Backup

D. Hari Chandra Prasad Babu Nayak — 2025-04-25

The increasing demand for sustainable and uninterrupted power supply has driven advancements in hybrid energy systems. This paper presents the design and implementation of an Arduino-based intelligent power switching and monitoring system for solar and grid hybrid energy sources. The system utilizes a solar panel to charge a battery through a charge controller, while also integrating grid power as a backup. A voltage sensor is used to monitor the battery level, and an Arduino microcontroller is employed to control a relay module, which intelligently switches the load between solar and grid power based on predefined thresholds. The LCD module displays real-time system status, enhancing user interaction. Additionally, a battery charge adaptor is included to ensure backup charging when solar input is insufficient. This system optimizes the use of renewable energy while maintaining uninterrupted power supply to the load, making it ideal for smart homes, rural electrification, and energy-efficient applications. Experimental results demonstrate reliable switching behavior and effective power utilization, validating the system’s practical viability.

Intelligent Load Balancing Framework for Optimal Resource Utilization in Fog-enabled IoMT Environment

Malaram Kumhar — 2025-04-25

The rapid adoption of Internet of Things (IoT) technologies in healthcare has given rise to the Internet of Medical Things (IoMT), which has transformed patient care and medical services. The IoMT, when combined with Fog Computing, provides a powerful paradigm for processing and analyzing healthcare data at the network edge. This paper proposes an innovative intelligent load balancing framework designed specifically for fog-enabled IoMT environments for optimizing resource utilization, improving system performance, and ensuring timely and efficient healthcare service delivery. The framework dynamically distributes computing tasks among fog nodes based on real-time parameters such as node capacity, latency, and workload. By combining machine learning (ML) models and data analytics, the system adapts to changing patterns in medical data, ensuring adaptive load distribution and faster response times. The proposed framework addresses the unique challenges facing healthcare applications, such as low latency and energy consumption in data transmission.