Daily Archives: October 4, 2025

Authors: Prof. Mayanka Roy Mandal, Prof. Shraddha Tiwari, Prof. Ankita Fouzdar

Abstract: The rapid growth of electric vehicles (EVs) has created a strong demand for compact, reliable, and high-efficiency DC–DC converters to ensure effective power management and extended driving range. This study focuses on the development of high-efficiency DC–DC converters specifically designed for EV applications, addressing challenges such as wide input voltage variations, high power density, and stringent thermal constraints. Advanced topologies including interleaved, resonant, and soft-switching techniques are explored to minimize switching losses and improve overall efficiency. Furthermore, integration of digital control strategies and advanced semiconductor devices such as SiC and GaN MOSFETs enhances performance while reducing converter size and weight. Simulation and experimental results demonstrate improved efficiency, voltage regulation, and transient response under dynamic load conditions. The proposed converters are shown to meet the critical requirements of modern EV powertrains, offering a sustainable solution for future electric mobility.

Published by: vikaspatanker

Authors: Prof. Shraddha Tiwari, Prof. Mayanka Roy Mandal, Prof. Ankita Fouzdar

Abstract: The integration of solar photovoltaic (PV) systems into the electrical grid requires efficient power electronic interfaces to ensure reliable operation and maximum energy extraction. This study focuses on the design and performance analysis of a power electronic interface for grid-connected solar PV systems incorporating Maximum Power Point Tracking (MPPT) techniques. A DC–DC converter controlled by MPPT algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC) is employed to optimize the PV output under varying irradiance and temperature conditions. The conditioned DC power is subsequently converted into synchronized AC power through a voltage source inverter (VSI) with appropriate grid synchronization and control strategies. The proposed system enhances the efficiency, stability, and power quality of PV-grid integration while minimizing harmonic distortion and ensuring compliance with grid codes. Simulation and experimental results validate that the implementation of an optimized MPPT-based power electronic interface significantly improves energy harvesting capability and supports sustainable and reliable integration of renewable energy into the power grid.

Published by: vikaspatanker

Authors: Prof. Ankita Fouzdar, Prof. Mayanka Roy Mandal, Prof. Shraddha Tiwari

Abstract: The rapid growth of renewable energy sources (RES) such as solar and wind has created new opportunities for sustainable power generation, while also posing significant challenges due to their intermittent and unpredictable nature. Smart grids, equipped with advanced communication and control technologies, offer a promising platform for efficiently integrating these variable energy resources. This study explores the optimal integration of renewable energy into smart grids using artificial intelligence (AI)-based forecasting and optimization techniques. Machine learning and deep learning models are employed to accurately predict renewable generation and demand patterns, reducing uncertainty and enabling proactive grid management. Furthermore, advanced optimization algorithms such as genetic algorithms, particle swarm optimization, and reinforcement learning are applied to achieve optimal scheduling, load balancing, and energy storage utilization. The proposed framework enhances grid stability, minimizes energy losses, reduces reliance on fossil fuels, and ensures cost-effective and reliable power delivery. Simulation results validate the effectiveness of the AI-driven approach in improving renewable energy penetration and overall smart grid performance. This work highlights the potential of AI-enabled forecasting and optimization as key enablers for achieving sustainable, resilient, and intelligent energy systems

Published by: vikaspatanker

Authors: I.C Emeto, B.P Gbaranwi, A.A. Galadima, A.C Okoloegbo, S. Kwaghbee, E.C Ochuba

Abstract: Cloud computing has emerged as a dominant paradigm for delivering scalable, on-demand computing resources, yet efficient load balancing remains a critical challenge in modern data centers. This paper presents a novel Hybrid Bee Ant Colony (HBAC) Algorithm that synergistically combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) metaheuristics to address the inherent limitations of existing load-balancing approaches. The proposed HBAC algorithm leverages ABC's robust exploration capabilities to identify underutilized virtual machines (VMs) and ACO's pheromone-driven exploitation mechanism to optimize task allocation, thereby achieving superior performance in dynamic cloud environments. Through extensive simulations using CloudSim with Google Cluster Data traces, we demonstrate that HBAC significantly outperforms standalone ACO and ABC algorithms across key performance metrics. Experimental results show 15.7% reduction in makespan, 22.3% improvement in response time, and 18.9% better resource utilization compared to conventional approaches. The hybrid model particularly excels in maintaining balanced VM workloads (degree of imbalance reduced by 27.4%) while demonstrating exceptional scalability under varying workload conditions (from 1,000 to 10,000 tasks). The algorithm's innovative two-phase architecture – where ABC scouts first identify high-potential VMs and ACO ants then optimize task placement – effectively overcomes the slow convergence of pure ACO and the excessive exploration of pure ABC. Energy efficiency analysis reveals 13.2% reduction in power consumption, making HBAC particularly suitable for sustainable cloud operations.

DOI: http://doi.org/10.5281/zenodo.17264412

Published by: vikaspatanker

Authors: Samiksha R Hajare, Prof. S. V. Raut

Abstract: Integration of blockchain technology into healthcare systems, particularly within telehealth and telemedicine frameworks, represents a paradigmatic shift aimed at resolving persistent challenges in digital health infrastructures. These challenges primarily encompass the secure exchange of medical data, achieving interoperability between disparate health information systems, and empowering patients with greater control over their personal health information. Blockchain enabled paradigms in healthcare are presented as transformative frameworks designed to address persistent issues such as secure medical data exchange, interoperability, and patient-centric control. The theoretical discussions around blockchain in healthcare highlight its potential complexities, with influence its research and practical use. The growth of telehealth and telemedicine has changed how healthcare is delivered, allowing for remote consultations and better resource management. However, current telemedicine systems often use centralized architectures, making them vulnerable to security threats like data breaches and fraud. This paper suggests incorporating blockchain technology into telemedicine platforms to improve security, transparency, and data integrity. By using a decentralized and tamper-resistant ledger, the proposed system aims to protect patient records and increase trust among healthcare providers and patients. Key features include secure appointment scheduling and reliable management of electronic health records within a user-friendly interface. This research helps advance telemedicine by addressing key security challenges and proposing a scalable, secure platform, especially useful in areas with limited access to traditional healthcare.

DOI: http://doi.org/10.5281/zenodo.17304779

Published by: vikaspatanker