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International Journal of Applied Power Engineering (IJAPE)
Published by Institute of Advanced Engineering and Science
ISSN : 22528792     EISSN : 27222624     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
International Journal of Applied Power Engineering (IJAPE) focuses on the applied works in the areas of power generation, transmission and distribution, sustainable energy, applications of power control in large power systems, etc. The main objective of IJAPE is to bring out the latest practices in research in the above mentioned areas for efficient and cost effective operations of power systems. The journal covers, but not limited to, the following scope: electric power generation, transmission and distribution, energy conversion, electrical machinery, sustainable energy, insulation, solar energy, high-power semiconductors, power quality, power economic, FACTS, renewable energy, electromagnetic compatibility, electrical engineering materials, high voltage insulation technologies, high voltage apparatuses, lightning, protection system, power system analysis, SCADA, and electrical measurements.
Arjuna Subject : -
Articles 614 Documents
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Predictive modeling and optimization of paper mill using hybrid machine learning techniques Abhijit Singh Bhakuni; Sandeep Kumar Sunori; Pradeep Juneja
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i2.pp692-702

Abstract

The paper has played a vital role in the life of humans from ancient times covering a vast range of applications such as writing, packaging, and printing. The present paper is presenting a comprehensive review of various optimization and control methodologies, ranging from conventional to advanced ones, pertaining to the paper mill. The final goal of these control strategies is to upgrade the mill’s production and quality in presence of multiple technical challenges such as nonlinear and multivariable nature of the involved processes, various disturbance parameters, and time delays. In this work, the integration of machine learning with paper mill process is illustrated. For any manufacturing process, the final product quality is the key goal. There are various traditional techniques which have already been practiced for final produced paper quality in paper mills. This paper highlights the capability of support vector machine (SVM) algorithm to assess the produced paper quality, capturing the two crucial inputs viz. the pulp consistency and the headbox level. The basic goal of this research is twofold, firstly it presents an exhaustive literature survey exploring various strategies which are practiced currently in the domain of control and optimization of various paper mill processes. Secondly, it intends to develop and evaluate various SVM and SVM-RF hybrid models using MATLAB for assessment of quality of final product on basis of two parameters- pulp consistency and head box level. Finally, genetic algorithm has been employed in MATLAB for multivariate optimization.
Intelligent gear shifting in electric and hybrid vehicles: a CAN controller-based approach using SOC% Kalagotla Chenchireddy; Naresh Jella; Vadthya Jagan; R. Naveena Bhargavi; Shabbier Ahmed Sydu; Nunavath Praveen
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i2.pp581-589

Abstract

The intelligent management of gear shifting in electric and hybrid vehicles (EVs and HEVs) is essential for optimizing energy efficiency, improving fuel economy, and enhancing driving comfort. Traditional gear shifting strategies, which are designed for internal combustion engine (ICE) vehicles, do not fully accommodate the unique dynamics of electric and hybrid powertrains. This paper proposes a novel approach for gear shifting in EVs and HEVs, integrating the state of charge (SOC%) of the battery as a critical input for decision-making. The proposed algorithm utilizes real-time data from the vehicle's controller area network (CAN), enabling seamless communication between the transmission control unit, battery management system, and powertrain control module. The algorithm adjusts gear shifting based on SOC%, vehicle speed, engine RPM, and throttle position, ensuring optimal use of the electric motor and internal combustion engine. At high SOC%, the algorithm prioritizes electric motor use to conserve fuel and extend battery life, while at lower SOC%, it switches to relying more on the combustion engine. The proposed method optimizes energy usage, enhances fuel efficiency, and prolongs battery life by adapting the shifting strategy to varying driving conditions.
Optimizing real-time energy control in hybrid low-voltage microgrids using a multi-agent approach Doha El Hafiane; Abdelmounime El Magri; Ilyass El Myasse; Adil Mansouri; Rachid Lajouad
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i2.pp505-513

Abstract

This research proposes a real-time framework for energy management and control in hybrid low-voltage microgrids (LVMGs) through multi-agent systems (MAS). The proposed framework enables decentralized and autonomous coordination among renewable energy sources, energy storage systems, loads, and the utility grid to dynamically optimize power flows under varying operating conditions. Each agent adjusts its setpoints using local information while cooperating with other agents to achieve global objectives. The MAS is implemented using The Java Agent Development Framework (JADE) and co-simulated with MATLAB/Simulink to accurately represent the microgrid’s physical behavior. Simulation results under grid-connected and islanded modes demonstrate that the proposed approach increases renewable energy utilization by up to 10% and reduces total energy costs by 7.6% compared to conventional centralized control schemes. Moreover, the system exhibits strong adaptability and robustness in the presence of renewable intermittency and load fluctuations, ensuring reliable real-time operation. These results confirm that MAS-based control provides an effective, scalable, and resilient solution for real-time energy management in hybrid LVMGs.
Advanced strategy for energy management and voltage stability in microgrid-a review Aswathi Ravindran; B. Rubini
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i2.pp880-893

Abstract

Microgrids (MGs) have emerged as transformative solution for improving energy resilience, stability, and sustainability in modern power systems. By incorporating distributed energy resources (DERs), renewable energy sources (RES), and energy storage systems (ESS), microgrids can supply reliable and stable power to local loads while also supporting the main grid during disturbances. Despite their potential, the efficient operation of MGs depends heavily on well-designed energy management and control systems (EMCS). A key challenge lies in addressing inherent variability of RES such as solar and wind, which introduces uncertainty in generation, as well as the dynamic and unpredictable nature of consumer loads. These factors make strong, adaptive, and intelligent energy management strategies crucial for ensuring both voltage stability and reliable operation. This paper presents review of advanced strategies developed for energy management and voltage stability in microgrids. It explores state-of-the-art optimization techniques, intelligent control methods, and emerging management frameworks that aim to balance generation, storage, and load demand efficiently. The study critically analyzes current methodologies, highlights their limitations, and identifies crucial research gaps in literature. By synthesizing recent developments, the paper provides insights in to innovative approaches that can enhance system reliability, optimize resource utilization, and ensure stable microgrid operation under uncertain conditions.

Page 62 of 62 | Total Record : 614

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