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30,938 Article Results

Comparative analysis of PM6:L8-BO organic and inverted organic solar cell

10.11591/ijape.v15.i2.pp770-780
Karthika Krishnakumar , Ashish Grover , Pardeep Kumar
Advancements in solar technologies are driven by the pursuit of higher efficiency and reduced environmental impact. This study presents a comprehensive and comparative analysis of organic and inverted organic solar cells (OSC and IOSC), using the OghmaNano software for simulations and analysis. This work is specifically designed to compare conventional and inverted structures and understand how device engineering impacts performance metrics. When OSCs are characterized by a low work-function cathode on top, IOSCs feature a clear conductive oxide cathode at the bottom. The study focusses on extracting key electrical output, including short circuit current density (JSC), open-circuit voltage (VOC), fill factor (FF) and power conversion efficiency (PCE), through the calculated current-voltage characteristic (J-V). Various physical characteristics, such as thickness of different layers and materials deployed as electron transport layer (ETL) and hole transport layer (HTL), are systematically investigated. Diverse top and bottom electrodes, encompassing monothin and multithin layer configurations, are proposed. The study shows that IOSC achieves higher efficiency than OSC, reaching 21.60%, while using a multithin layer ZTZ (ZnO/TiOx/ZnO) as the bottom contact, demonstrating improved charge transport and overall efficiency.
Volume: 15
Issue: 2
Page: 770-780
Publish at: 2026-06-01

Enhancing grid performance through coordinated SVC-TCSC operation with PV support: A case study on IEEE 30-bus system under progressive loading

10.11591/ijpeds.v17.i2.pp1254-1264
Hafidha Reriballah , Latifa Smail , Ali Abderrazak Tadjeddine , Hocine Guentri , Rim Feyrouz Abdelgoui , Fatima Zohra Boudjella
Power systems face growing challenges of voltage instability, line congestion, and increased losses under rising demand. This study proposes a coordinated approach using two flexible AC transmission system (FACTS) devices: the static var compensator (SVC) and the thyristor controlled series capacitor (TCSC), together with photovoltaic (PV) generation, to enhance grid performance. The IEEE 30 bus test system is analyzed under normal and increased load conditions (5%, 10%, 15% load growth). Results show that coordinated SVC TCSC operation improves voltage profiles, reduces critical line loading by 14%, and lowers active and reactive losses by 10% and 23.8%, respectively, in the base case. Under a 15% load increase, integrating a 25 MW PV system with the coordinated FACTS restores the minimum voltage to 0.95 p.u., reduces line congestion by 27%, and decreases active and reactive losses by 35.5% and 53.5%. The combined FACTS PV strategy proves essential for maintaining stability and efficiency under high load growth. This integrated approach provides practical guidance for transmission operators toward resilient, loss aware, and renewable integrated smart grids.
Volume: 17
Issue: 2
Page: 1254-1264
Publish at: 2026-06-01

Improved control strategy for harmonic current mitigation in DFIG-based wind turbines supplying linear and nonlinear loads

10.11591/ijpeds.v17.i2.pp933-945
Hind Elaimani , Noureddine Elmouhi
Improving power quality is a major challenge in grid-connected wind energy systems, especially under mixed linear and nonlinear load conditions. This paper proposes an enhanced control strategy for harmonic current mitigation in a doubly fed induction generator (DFIG)-based wind turbine. The proposed approach integrates flux-oriented vector control with an active harmonic compensation algorithm implemented through the rotor-side converter (RSC). Unlike conventional methods that target only specific harmonic orders, the proposed strategy mitigates all current harmonics at the point of common coupling (PCC). Simulation studies conducted under various load conditions demonstrate that the method significantly reduces the total harmonic distortion (THD) and ensures near-sinusoidal stator currents. The results confirm the effectiveness and robustness of the proposed control approach in improving the power quality of DFIG-based wind energy conversion systems.
Volume: 17
Issue: 2
Page: 933-945
Publish at: 2026-06-01

Optimal selection of current control technique in multiphase DC-DC converters for dynamic load variations

10.11591/ijpeds.v17.i2.pp991-1007
H. Swathi Hatwar , K. Suryanarayana , Anup Shetty
Multiphase DC-DC converters are widely adopted in high-power applications such as electric vehicles (EVs) and renewable energy systems due to their ability to reduce current ripple, improve efficiency, and distribute thermal stress across multiple phases. However, under dynamic load variations, mismatches in passive components, device parameters, parasitic elements, and thermal effects can result in phase current imbalance. This imbalance degrades transient performance, increases circulating currents, and reduces overall system reliability. Therefore, selecting an appropriate current control strategy is essential to ensure accurate current sharing and stable output voltage regulation under varying operating conditions. This paper presents a comparative study and selection methodology for current control techniques for MCU-based interleaved DC-DC converters. Various current control strategies are evaluated in terms of dynamic response, steady-state current sharing accuracy, implementation complexity, and embedded feasibility. A 1 kW, 36 V-12 V three-phase interleaved buck converter using Gallium Nitride devices is modeled in MATLAB/Simulink and validated through hardware experimentation. The comparative results highlight the trade-offs among transient performance, current balancing accuracy, scalability, and embedded implementation complexity, providing a structured basis for selecting an appropriate current control technique as per application requirements.
Volume: 17
Issue: 2
Page: 991-1007
Publish at: 2026-06-01

Predicting Indonesian academician turnover intention: validity and reliability analysis

10.11591/ijaas.v15.i2.pp479-489
Faisal Al Abid , Aryati Bakri , Hasin Jawad Ali , Darmawan Satyananda , Shefayatuj Johara Chowdhury , Jia Uddin
This study evaluates Indonesian academic turnover intention (TOI) by analyzing demographic and work-related factors through feature selection methods and utilizes random forest (RF) as a baseline classifier for TOI prediction, while applying statistical methods to ensure the reliability of the collected primary dataset. The main advantage of this approach is to find out the importance of these factors with statistical validation to reliably investigate Indonesian academicians’ TOI. Feature selection methods such as information gain (IG) and SelectKBest were used to find out feature importance, while the reliability of the dataset was assessed through statistical approaches such as Cronbach alpha, confirmatory factor analysis (CFA), average variance extracted (AVE), and consistency ratio (CR). To test the importance of demographic and work-related factors, Python was used as an implementation tool for the Indonesian academic TOI dataset (IRB reference: 19.12.4/UN32.14/PB/2024), comprising 527 samples. The superiority of the importance of work-related factors in contrast to demographic factors was consistently demonstrated by feature selection methods, and a statistical approach confirmed the reliability of the collected primary dataset, consequently ensuring the robustness of the findings. It is envisaged that this approach can be very useful for human resource (HR) departments to pay more attention to the important demographic factors for reducing Indonesian academic TOI.
Volume: 15
Issue: 2
Page: 479-489
Publish at: 2026-06-01

Network traffic analysis and bandwidth forecasting for using Meta’s Prophet: a case study

10.12928/telkomnika.v24i3.27609
Yusuf Onimisi; Landmark University Isaac , Ayodeji James; Tshwane University of Technology Bamisaye , Ijagbemi; Landmark University Adedotun , Theophilus Olusegun; Landmark University Dada , Onyemenam Obiajulu; Landmark University John
This study created a forward-looking bandwidth prediction system for students’ halls of residence at Landmark University. The system uses Meta’s Prophet, a method for analyzing patterns in data over time, and was trained on past internet traffic data from October to December 2024. The system was able to predict future bandwidth usage with over 90% accuracy. To assess how well the system worked, several common metrics were used, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The MAE was calculated as 10,099,863.10 bits per second (bps), and the RMSE was 13,570,959.58 bps. While the mean squared error (MSE) appears large numerically, this is anticipated due to the size of the bandwidth data involved in its calculation. Importantly, the prediction errors are considered reasonable when considered in relation to the actual peak bandwidth usage, which fluctuated between 47 and 50 megabits per second (Mbps). These findings suggest that machine learning can be a valuable tool for refining network infrastructure and improving the user experience quality of service (QoS) in environments with many users, such as university residences.
Volume: 24
Issue: 3
Page: 751-764
Publish at: 2026-06-01

High-gain DC-DC converter with advanced techniques: a review

10.11591/ijpeds.v17.i2.pp1105-1117
Anitha Sagari Ravirala , T. Vijay Muni , T. Vinodita , K. Venkata Kishore , Ramoju Bheema Sankaram , Yuriy Yu Shvets
This article provides an in-depth examination of recent advances in high-gain DC-DC converters, emphasizing soft-switching techniques and topological innovations that minimize voltage stress for renewable energy applications. High-gain DC-DC converters are crucial in photovoltaic and fuel-cell systems, where boosting low input voltages to higher levels must be achieved with high efficiency and compact design. Traditional boost converters fall short due to elevated switching stress, discontinuous input currents, and lower efficiency at high-gain levels. To address these limitations, this review categorizes and critically evaluates state-of-the-art converter topologies developed for high-gain operation. The main contributions of this review are as follows: i) A systematic classification of high-gain converter configurations with emphasis on their operational principles; ii) A detailed evaluation of soft-switching techniques, including zero voltage switching (ZVS) and zero current switching (ZCS), focusing on their roles in reducing switching losses and electromagnetic interference; iii) An analytical discussion on voltage stress mitigation methods and improved control strategies; and iv) An assessment of emerging trends in integrating advanced power electronics with renewable energy systems. These contributions collectively provide a comprehensive reference for researchers and engineers, supporting the development of next-generation high-performance DC-DC converters tailored for sustainable energy applications.
Volume: 17
Issue: 2
Page: 1105-1117
Publish at: 2026-06-01

Resilient EV charging station network design using AI algorithms

10.11591/ijpeds.v17.i2.pp1543-1552
Deepa Somasundaram , N. Krishnamoorthy , J. Vijay Anand , R. Priyanka , T. Santhana Krishnan , Kirubakaran Dhandapani
This paper proposes an AI-driven resilient network design framework for optimal electric vehicle (EV) charging station placement under stochastic demand and dynamic grid constraints. The proposed approach uniquely integrates long short-term memory (LSTM) based spatiotemporal demand forecasting with a hybrid genetic algorithm-particle swarm optimization (GA-PSO) model for multi-objective station placement. In addition, a deep reinforcement learning (DRL) agent is incorporated to enhance adaptive resilience under real-time grid disturbances. The framework minimizes installation cost, reduces user travel distance, and improves grid stability while ensuring equitable accessibility. The model is evaluated under multiple scenarios, including peak demand, station outages, renewable intermittency, and grid capacity reduction. Results demonstrate that the proposed hybrid AI framework achieves a resilience index of 0.92, reduces travel distance by 54%, and lowers installation cost by up to 16% compared to conventional approaches such as linear programming (LP) and K-means clustering. The integration of renewable energy further reduces peak grid dependency by 18%. The proposed methodology provides a scalable and practical solution for designing sustainable and resilient EV charging infrastructure in smart urban environments.
Volume: 17
Issue: 2
Page: 1543-1552
Publish at: 2026-06-01

A novel single-stage high-voltage gain DC-DC boost converter for on-board PEV charging system

10.11591/ijape.v15.i2.pp610-619
Motepalli Siva Rama Ganesh , S. Sasikumar , B. Suresh Babu
Currently, the utilization of plug-in electric vehicles is quickly increasing in the vehicle industry owing to reduced costs of transportation, no need for fossil fuels, simple servicing, no fuel expense, and lower environmental effect compared to internal-combustion motor vehicles. In actuality, these motor vehicles function based on available battery energy that are charged by a utility-grid-supplied charging station. In this charging facility, a power converter defined on-board charger is generally used to charge the batteries, which improves the utility grid specifications by reducing the presence of harmonics and power factor regulation. An active two-stage load conditioning approach is commonly employed, however it doubles the conversion stages, requires larger switching components, complicated circuitry, large switching losses, and decreased efficiency, among other issues. To address these issues, a unique single-stage on-board EV charger has been used to regulate utility-grid specifications and seamless management of battery state-of-charge using a load-side DC-DC conditioning method. The major goal of this study is to propose a unique DC-DC boost converter that provides substantial voltage gain, consistent input current, minimal current ripples, and highest efficiency among numerous converters. The effectiveness of the proposed unique single-stage on-board EV charger has been evaluated through MATLAB/Simulink application, and the simulation findings have been presented.
Volume: 15
Issue: 2
Page: 610-619
Publish at: 2026-06-01

Comparison of differential evolution optimization technique with other techniques in solving multi-objective optimal power flow

10.11591/ijape.v15.i2.pp663-673
Vineeta S. Chauhan , Jaydeep Chakravorty , Siddharthsingh K. Chauhan
Optimal power flow (OPF) is a complex, non-linear optimization problem focused on determining the steady-state operating parameters of power systems for economic and secure operation. The challenge intensifies due to numerous system constraints that must be satisfied simultaneously. Although various evolutionary algorithms (EAs) have been applied to OPF in recent decades, these algorithms often use unconstrained search strategies. A common approach to handle constraint violations is the static penalty function, which penalizes infeasible solutions. However, selecting suitable penalty coefficients typically involves time-consuming trial and error, affecting overall performance. This study explores the integration of advanced constraint handling (CH) techniques within the differential evolution (DE) framework to enhance the performance of optimal power flow (OPF) solutions. In particular, it looks at three approaches: a hybrid ensemble of two CH techniques (ECHT), a self-adaptive penalty method (SP), and superiority of viable solutions (SF). The IEEE 30-bus and IEEE-57 bus benchmark systems are used to evaluate the efficacy of these techniques under a variety of OPF goals, including lowering emissions and generation costs, cutting power losses, and enhancing voltage stability. We took into consideration both weighted-sum multi-objective and single-objective formulations. The simulation outcomes indicate that the proposed CH-DE approaches deliver robust and competitive optimization results, demonstrating improved constraint handling capabilities when compared to contemporary methods in the literature.
Volume: 15
Issue: 2
Page: 663-673
Publish at: 2026-06-01

Wind direction based aggregation of wind power plants under exact wind speeds

10.11591/ijape.v15.i2.pp818-830
Ali M. S. Al-Bayati , Huda Hamza Abdulkhudhur
Modeling of a wind power plant (WPP) containing numerous wind turbines in a highly detailed manner requires a substantial computational cost. Further, the response and dynamic behavior of the WPP systems are significantly influenced by the dynamic nature of wind speed. This paper presents a methodology of aggregating WPP systems with consideration for wind speed directions. To attain a realistic aggregated model, an algorithm for calculating actual exact wind speed at each wind turbine within the WPP was proposed considering different wind speed directions. Furthermore, the best wind speed direction for a fixed site area that produces a minimum wind energy losses inside the WPP was also assessed and reported. The results revealed the importance of employing the exact wind speed calculations within the WPP to ensure that the aggregated WPP model accurately represents real-world conditions. The results of this paper highlighted the role of wind direction in determining the response of WPPs and provide guidance on maximizing the WPP throughput during the year under the prevailing wind speed direction at the site.
Volume: 15
Issue: 2
Page: 818-830
Publish at: 2026-06-01

Sliding mode control of a solar powered switched-inductor based quadratic DC-DC converter for sustainable EV battery charging application

10.11591/ijape.v15.i2.pp712-723
Jawahar Marimuthu , Edward Rajan Samuel Nadar
The growing demand for sustainable transportation and fast charging solutions requires efficient power conversion technologies for solar electric vehicles or electric vehicles (SEVs/EVs). A non-isolated solar-powered switched-inductor quadratic DC-DC converter is proposed here to achieve high voltage gain in a practical way under reduced stress on power devices. A switched-inductor network blended with CCM operation avoids the extremely high duty cycles and high electromagnetic interference in conventional boost converters. A sliding mode control (SMC) strategy is applied here to improve robustness against parameter variations, ensure stable operation against dynamic load variations, and extract maximum power during solar-powered charging operation. This makes the topological platform proposed in this study especially suitable for a wide variety of applications, such as for SEVs and fast-charging applications of EVs. Detailed MATLAB/Simulink analyses along with a laboratory-scale prototype verify the performance of the converter under practical operation conditions and confirm the high efficiency of 91-96% at varied irradiance, low voltage ripple of 0.5-1.5% of output voltage and input current ripple of 5-12% of input current, reduced switching losses of 1-4%, and suitability of the presented converter for renewable-energy-based transportation systems.
Volume: 15
Issue: 2
Page: 712-723
Publish at: 2026-06-01

Mathematical modelling and automated control strategies for sugarcane crushing system of sugar factory

10.11591/ijape.v15.i2.pp554-564
Govind Singh Jethi , Sandeep Sunori , Surya Kant , Pradeep Juneja
Mathematical models form the basis of automation and digitalization. Control and optimization of industrial processes are important for increasing productivity and efficiency, especially in the sugar industry. This research focuses on modeling and controlling the juice extraction process, which is an important activity in sugar production. The mathematical model is obtained by creating a variable based on simple equations where the cane level in the Donnelly channel is the input and the juice output. The model captures the complexity of the process and provides a solid basis for the design of control systems. Two advanced control concepts: H-infinity control and model control (MPC) were used in MATLAB to meet the criteria. While H-infinity control provides performance in the presence of uncertainty and disturbances, MPC optimizes control performance by predicting future results. This paper observes and compares the results of two control systems to analyze their performance. This comparison highlights the advantages and limitations of each method. The research results are of great importance for increasing the efficiency and reliability of industrial processes in the sugar industry.
Volume: 15
Issue: 2
Page: 554-564
Publish at: 2026-06-01

Analyzing the ability of capacitor energy in a modular multilevel converter to support inertia in an AC system

10.11591/ijape.v15.i2.pp646-662
Dunya Sh. Wais , Huda A. Abbood
Flexible DC transmission systems based on modular multilevel converters have the potential to support the inertia of AC power grids by using sub-module capacitor energy storage. However, existing studies generally believe that the inertia provided by flexible DC systems is limited by their energy storage time constants, which is weaker than that of synchronous motors, and lacks quantitative indicators to measure their support strength. Introducing the flexible-DC equivalent inertia constant (FDEIC) as a precise metric for assessing inertia support under different management schemes, this research presents a new analytical framework based on frequency responses. Results show that the inertial response is influenced by control bandwidth, DC-voltage dynamics, and circulating-current behaviour. A more generalized multi-terminal FDEIC is created to account for the impact of raised total capacitor energy, and the theory is further expanded to cover DC grids with more than one terminal. A three-terminal flexible DC grid simulation model is built in the PSCAD environment, and the simulation results verify the effectiveness of the proposed quantitative analysis method.
Volume: 15
Issue: 2
Page: 646-662
Publish at: 2026-06-01

Hourly scheduling of thermal units utilizing an innovative hybrid approach

10.11591/ijape.v15.i2.pp600-609
Vempalle Rafi , Shaik Hussain Vali , Sadhu Radha Krishna , Uppuluri Suryavalli , M. Rajesh , Sayapogu Prateepkumar
The producing unit must be turned on at a time that meets the power system network's needs. It also determines the order of unit shutdowns based on cost. Unit commitment includes computation and turning units on and off. Committed units are planned to join the power system network. The combinatorial character of unit commitment makes it a crucial research issue and optimization job in contemporary power system. In order to effectively use the available resources and equalize the load demand on an hourly basis, unit commitment might be used. In order to solve an optimization issue involving unit commitment, this work introduces a new hybrid approach that combines a whale optimization algorithm (WOA) with a self-organizing migration algorithm (SOMA). An important part of any migration loop is the WOA technique, which is used to evaluate the optimum strength population from the populations that are created stochastically. The suggested hybrid approach is evaluated using two test systems. Before moving on to the IEEE 39 bus system, a four-unit system is implemented. The efficiency of the suggested hybrid WAOSOMA is addressed by comparing the generated simulation results with approaches found in the literature.
Volume: 15
Issue: 2
Page: 600-609
Publish at: 2026-06-01
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