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30,609 Article Results

Moth flame optimization based super twisting sliding mode MPPT controller for grid connected PV system

10.11591/ijape.v15.i2.pp703-711
Ujwala Gajula , Gouthami Eragamreddy , N. Malla Reddy , Remala Geshma Kumari , Veeranjaneyulu Gopu
Maximizing energy extraction while maintaining the stability of solar photovoltaic (PV) systems requires an effective and robust control strategy. This study proposes a novel control approach by integrating a super twisting sliding mode controller (STSMC) with the moth-flame optimization (MFO) algorithm to enhance battery energy management, power quality, and maximum power point tracking (MPPT) in grid-connected PV systems. The proposed MFO-STSMC controller combines the robustness of sliding mode control with the adaptive optimization capabilities of MFO, resulting in improved MPPT accuracy, reduced oscillations, and enhanced resilience to environmental disturbances and nonlinearities. Simulation results validate that the proposed method significantly outperforms conventional MFO-PI controllers, achieving accurate MPPT tracking under varying irradiance and temperature conditions, and ensuring stable operation. Moreover, the total harmonic distortion (THD) is reduced to 0.17% with MFO-STSMC, compared to 0.72% with MFO-PI, highlighting substantial improvement in power quality. The system is modeled and validated using MATLAB/Simulink, confirming the effectiveness of the proposed strategy in enhancing energy efficiency and grid stability.
Volume: 15
Issue: 2
Page: 703-711
Publish at: 2026-06-01

Technological and philosophical perspectives on photovoltaic electric bicycles for sustainable mobility

10.11591/ijape.v15.i2.pp905-914
Endang Susanti , Azriyenni Azhari Zakri , Antonius Rajagukguk
This paper investigates photovoltaic-based electric bicycles (PV e-bikes) as sustainable transportation solutions through integrated technological and ethical analysis. Our systematic literature review and mathematical modeling examine technological advancements, environmental benefits, and implementation challenges. Key findings reveal PV e-bikes can extend travel range by 20.5 km on sunny days and reduce annual grid charging needs by 93% in optimal locations. Advanced perovskite solar cells achieving 25.7% efficiency show promise for revolutionizing on-the-go charging. Mathematical analysis reveals optimal PV panel sizing requires 45-225 W capacity depending on integration approach, with battery technologies ranging from LiFePO₄ (2000-4000 cycles) to advanced lithium-ion systems. However, critical challenges persist including partial shading effects reducing efficiency by 65-82%, weather-dependent performance variations, and initial production costs of $467-900. The study addresses specific gaps in previous research by developing a comprehensive framework integrating technical performance metrics with ethical considerations. Novel contributions include: i) mathematical modeling of PV-battery optimization for e-bike applications, ii) systematic analysis of partial shading mitigation strategies, and iii) philosophical framework addressing social justice implications. Results demonstrate that while PV e-bikes offer significant environmental benefits, their widespread adoption requires addressing technological limitations, cost barriers, and equitable access concerns.
Volume: 15
Issue: 2
Page: 905-914
Publish at: 2026-06-01

Utilization of BSA optimized cascade controller in a renewable energy-based AGC systems

10.11591/ijape.v15.i2.pp546-553
Rambabu Kasukurthi , R. Srinu Naik
A novel cascade controller named proportional integral derivative-tilt integral derivative (PID-TID) is proposed for a two-area thermal-wind automatic generation control (AGC) system and its gains are optimized by a novel metaheuristic bird swarm algorithm (BSA). The BSA tuned PID-TID controller enhances dynamics over PID and TID controller in terms of settling time and peak shoots. Moreover, dynamics with wind integration have shown significant improvement over thermal system alone. Further system has shown enhanced dynamics with redox flow batteries (RFB) over thermal-wind system. Furthermore, studies with automatic voltage regulation (AVR) strengthen voltage stability. Also, responses with PID-TID have shown steady dynamic profile at various loading conditions. Integrating wind energy into thermal system results in significant enhancements in dynamics showcasing greater stability. Also, improvements are evident with the RFB introduction, enhance dynamic with in hybrid system. The incorporation of AVR enhance voltage stability. The proposed PID-TID demonstrates significant robustness ensuring stable response under loading condition and effectively boost dynamic performance.
Volume: 15
Issue: 2
Page: 546-553
Publish at: 2026-06-01

Hydroelectric power generator using vertical axis turbine with adaptive blades

10.11591/ijape.v15.i2.pp636-645
Rizki Nurilyas Ahmad , Soraya Komala Firdaus , Mohammad Nasrul Mubin , Hasyim Asy'ari , Tindyo Prasetyo , Iqbal Reza Pradana
The implementation of micro-hydro power plants (MHPPs) offers a strategic solution for achieving energy independence, particularly within remote communities. This study proposes the development of a hydroelectric power generator with a vertical axis turbine, designed not only as a source of clean energy but also to minimize visual pollution. The system maximizes submerged components, thereby reducing its visual impact. Although MHPPs technology is widely applied to address electrification challenges in remote areas, the system proposed in this study, with its components predominantly submerged below the water surface, offers a visually unobtrusive solution that is also well-suited for urban environments. However, conventional locked-blade turbines often experience significant efficiency losses due to counter-flow pressure acting on blades moving against the water stream, highlighting the need for an adaptive mechanism to minimize drag and optimize energy capture. The hydroelectric power generator using vertical axis turbine with adaptive blades consistently demonstrated better performance than a system using locked blades. The adaptive-blade configuration outperformed the locked-blade system, exhibiting a 5.1% increase in average turbine efficiency and a 3.5% improvement in overall system efficiency.
Volume: 15
Issue: 2
Page: 636-645
Publish at: 2026-06-01

Transient stability analysis of a new proposed hybrid PV-WTG microgrid for Tinghir power distribution

10.11591/ijape.v15.i2.pp449-463
Hicham Stitou , Mohamed Amine Atillah , Abdelghani Boudaoud , Mounaim Aqil
This work focuses on the transient stability of a hybrid photovoltaic and wind turbine generator (PV-WTG) system at the Tinghir 225/60/11 kV substation in Morocco. Results were obtained by evaluating the effects of the proposed configuration on power angle, frequency, voltage, and fault-clearing times in the system. The study examined key disturbances, including abrupt loss of renewable energy and major electrical faults. Analysis using ETAP demonstrated a power angle change of -55 degrees, 20 degrees greater than the normal operating point, which can be caused by the loss of PV and approaches the IEEE Std 421.5 stability limit. The maximum voltage variation was 6.1% for the PV and 2.7% for the WTG, exceeding the IEC 60034-1 limits of ±5%. Another major finding of this analysis was that WTG loss induces frequency swings of 0.8 Hz and requires 10 to 15 seconds for recovery, indicating that low-inertia systems have insufficient inertia to return to steady state quickly. Therefore, the study demonstrates that adaptive control approaches must be used to achieve stable operation of hybrid connected microgrids. Using the time domain simulation (TDS) process, we calculated the critical clearing time (CCT) of 155 ms for 3-phase faults and 464 ms for line-to-ground faults, all of which are within the CCT limit set by IEEE Std 3002.2, and this confirms the necessity of urgent clearing of faults to maintain transient stability and demonstrates the need for fast protection and adaptive control in low-inertia systems, which is of particular concern in rural grids.
Volume: 15
Issue: 2
Page: 449-463
Publish at: 2026-06-01

Performance assessment of PSO variants for optimal photovoltaic and DSTATCOM allocation in radial distribution networks

10.11591/ijpeds.v17.i2.pp946-957
Mohamed Kherchi , Hacene Mellah , Souhil Mouassa , Anwar Fellahi
This work presents a comparative evaluation of adaptive particle swarm optimization (PSO) variants for the optimal placement and sizing (OPS) of photovoltaic-based distributed generation (PV-DG) and DSTATCOM units in the standard IEEE 33-bus radial distribution network (RDN). Five adaptive PSO algorithms are investigated, namely adaptive acceleration coefficients PSO (AAC-PSO), autonomous particle groups PSO (APG-PSO), nonlinear dynamic acceleration coefficients PSO (NDAC-PSO), sine-cosine acceleration coefficients PSO (SCAC-PSO), and time-varying acceleration PSO (TVA-PSO). The optimization framework is structured as a single-objective problem focused on maximizing the active power loss index (APLI), which is used as a normalized indicator associated with active power loss reduction. To further assess the technical quality of the obtained solutions, two additional performance indicators are considered, namely the total voltage deviation (TVD) and the voltage stability index (VSI). The simulation outcomes indicate that the TVA-PSO algorithm exhibits superior overall performance compared to other evaluated variants in terms of convergence behavior and solution quality. In particular, it achieves the highest APLI value of 92.52%, corresponding to an active power loss reduction of 91.91%, with active power losses (APL) reduced from 210.99 kW to 17.07 kW. In addition, the obtained solution significantly improves the network voltage profile (VP) and enhances voltage stability. These findings provide evidence that the effectiveness of adaptive PSO strategies for optimizing PV-DG and DSTATCOM integration in RDN.
Volume: 17
Issue: 2
Page: 946-957
Publish at: 2026-06-01

Energy-aware dynamic adjustment integrated kookaburra optimization based efficient routing in WSN

10.11591/ijape.v15.i2.pp724-734
Shobanbabu R. Jaganathan , R. Sathya , R. Karthikeyan
In this paper a novel kookaburra optimization algorithm based dynamic adjustment strategy (KOA-DAS) method has been proposed in this paper for the energy efficient (EE) clustering and routing in wireless sensor network (WSN). The satin bowerbird optimization (SBO) is utilized for optimum cluster head (CH) selection. The proposed KOA-DAS model is utilized for an efficient routing through considering the fitness functions like distance from CH to base station (BS), remaining energy and intra-communication cost. The suggested framework has been assessed using a MATLAB simulator. The efficacy of the suggested KOA-DAS framework has been determined using evaluation metrics including execution time, average residual energy, network lifetime (NL), latency, packet delivery ratio (PDR), computation cost, energy consumption (EC), and alive nodes. The suggested KOA-DAS framework achieves the lowest energy efficiency by 23.44%, 19.31%, and 14.44% than the ASFO, EELCR, and K-LionER approaches. The proposed model effectively selects the CH and routing through dynamically adjusting parameters, which results in minimum EC and extending NL.
Volume: 15
Issue: 2
Page: 724-734
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

Adaptive telematics integration for enhanced EV fleet management and data acquisition

10.11591/ijape.v15.i2.pp808-817
Kavitha Kumaraswamy , Pasumarthi Usha , S. Ashok Kumar , Deekshitha Arasa , Suganthi Neelagiri
Telematic control units (TCUs) and on-board diagnostics (OBD-II) systems are commonly used to monitor vehicles and enable real-time communication. However, traditional OBD-II systems provide limited data, making it difficult to accurately detect faults and analyze performance, especially in hybrid, flex-fuel, and electric vehicles. A TCU is an embedded system installed in vehicles that enables wireless communication with external networks. This paper introduces a standalone device designed to seamlessly integrate with electric vehicles (EVs) by utilizing TCU capabilities to enhance data acquisition. The TCU uses a combination of sensors to collect important real-time vehicle data, such as GPS location, battery charge level, and voltage levels. The collected data is processed to generate meaningful insights that support decision-making and system optimization. The proposed system uses the TCU as a core component to transmit real-time data to a fleet management system (FMS). By providing enhanced data to the FMS, the system improves diagnostic accuracy, strengthens EV safety monitoring, and enables more efficient fleet management across diverse vehicle types. This approach allows deeper monitoring of EVs and improves overall fleet efficiency. The framework offers a cost-effective and scalable solution for advanced monitoring and optimization of electric vehicle fleets.
Volume: 15
Issue: 2
Page: 808-817
Publish at: 2026-06-01

Stochastic planning for feeding a green hydrogen plant into an isolated network

10.11591/ijape.v15.i2.pp744-759
Michael Salcedo , Mario A. Rios
In recent years, an electrochemical process called electrolysis has gained prominence. This process uses water and electricity as its main sources, significantly reducing the carbon footprint of hydrogen production. Additionally, colors have been assigned to represent the source of hydrogen production in a simple way. For example, green refers to hydrogen produced by electrolysis using electricity generated from non-conventional renewable energy sources (NCRES). For plants not connected to the national grid, the connection of a green hydrogen plant requires that NCRES be connected to an isolated electrical grid. In these cases, the power supply will depend on the variability of the source. This paper presents the methodology to plan and size the main components of the wind power plant and the battery energy storage system (BESS) to ensure that the electrolyzer constraints can be met during the studied period. Furthermore, it introduces a novel methodology that uses the autoregressive moving average (ARMA) model to generate a sequential Monte Carlo simulation along with dynamic optimization. This approach allows for the sizing of the wind power plant and BESS, considering the stochastic behavior of the wind.
Volume: 15
Issue: 2
Page: 744-759
Publish at: 2026-06-01

Design to optimize the location, number, and performance of dynamic voltage restorers using artificial neural networks

10.11591/ijape.v15.i2.pp793-807
Yulianta Siregar , Faizzufar Taqy , Mohd Najib Mohd Hussain , Hafizh Prihtiadi , Muldi Yuhendri
The need for electrical energy always increases from year to year. This means that the distribution system in the electric power system needs to pay attention to its level of stability and reliability. A low level of stability can cause disruption and result in losses. The system's stability and reliability can be increased by installing custom power devices (CPD) equipment such as a dynamic voltage restorer (DVR). In this research, the location, number, and performance of DVRs are optimized using an artificial neural network based on the voltage stability of the distribution network in the Sibolga Penyulang SB02 area. Based on the research results, buses 2, 12, 24, 27, and 35 are the best places to install DVRs, and the system will have five DVRs installed. A three-phase short circuit simulation was used to determine how feeder stability was impacted by DVR performance. Then, the voltage falls to 0.1770 p.u. during a disturbance and then rises to 0.8073 p.u., which is within the typical voltage limit of > 0.9 p.u. It means that DVRs restored the voltage fully to the acceptable threshold.
Volume: 15
Issue: 2
Page: 793-807
Publish at: 2026-06-01

Predictive modeling and optimization of paper mill using hybrid machine learning techniques

10.11591/ijape.v15.i2.pp692-702
Abhijit Singh Bhakuni , Sandeep Kumar Sunori , Pradeep Juneja
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.
Volume: 15
Issue: 2
Page: 692-702
Publish at: 2026-06-01

Optimizing real-time energy control in hybrid low-voltage microgrids using a multi-agent approach

10.11591/ijape.v15.i2.pp505-513
Doha El Hafiane , Abdelmounime El Magri , Ilyass El Myasse , Adil Mansouri , Rachid Lajouad
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.
Volume: 15
Issue: 2
Page: 505-513
Publish at: 2026-06-01

Surface passivation-induced enhancement of light absorption in photoanodes for quantum dot-based solar cells

10.11591/ijape.v15.i2.pp948-954
Ho Minh Trung , Le Xuan Thuy
Quantum dot-sensitized solar cells hold promise for low-cost, high-efficiency photovoltaic applications; however, instability due to quantum dot degradation and poor interfacial charge transport remain key challenges. In this study, a copper-doped Zn(S,Se) passivation layer was chemically synthesized and applied onto TiO₂/CdS/CdSe@Cu photoanodes. The goal was to shield quantum dots from corrosive polysulfide electrolytes and enhance photon absorption. The morphology, structure, and optical characteristics of the Zn(S,Se):Cu layers were systematically analyzed using field-emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and UV-Vis spectroscopy. J-V measurements demonstrated that the ZnSe:Cu-coated photoelectrode achieved a higher power conversion efficiency (5.31%) than the ZnS:Cu counterpart (4.5%). Moreover, electrochemical impedance spectroscopy revealed a lower charge transfer resistance (Rct2 = 331 Ω), indicating improved electron transport and reduced recombination. These findings highlight the potential of Zn(S,Se):Cu layers in enhancing the stability and efficiency of quantum dot-sensitized solar cells, paving the way for more durable and efficient solar energy devices.
Volume: 15
Issue: 2
Page: 948-954
Publish at: 2026-06-01

Intelligent gear shifting in electric and hybrid vehicles: a CAN controller-based approach using SOC%

10.11591/ijape.v15.i2.pp581-589
Kalagotla Chenchireddy , Naresh Jella , Vadthya Jagan , R. Naveena Bhargavi , Shabbier Ahmed Sydu , Nunavath Praveen
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.
Volume: 15
Issue: 2
Page: 581-589
Publish at: 2026-06-01
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