International Journal of Applied Power Engineering (IJAPE)
International Journal of Applied Power Engineering (IJAPE), p-ISSN 2252-8792, e-ISSN 2722-2624 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 and electrical energy sources. The journal covers, but is not limited to, the following scope: electric power generation, transmission and distribution, utilization of electric power, 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, electrical measurements, and the electrical apparatus connected to such systems. The journal is published by the Intelektual Pustaka Media Utama (IPMU) in collaboration with Institute of Advanced Engineering and Science (IAES). Beginning with issue 1 of volume 10 (2021), the IJAPE will be published as a three-monthly journal (4 issues/year). We are happy to announce that the International Journal of Applied Power Engineering has been accepted for inclusion in Scopus, the world's largest abstract and citation database of peer-reviewed literature, and got Q2 in the journal ranking by ScimagoJR.
Institute of Advanced Engineering and Science
Transient stability analysis of a new proposed hybrid PV-WTG microgrid for Tinghir power distribution
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.
Copyright (c) 2026 Hicham Stitou, Mohamed Amine Atillah, Abdelghani Boudaoud, Mounaim Aqil http://creativecommons.org/licenses/by-sa/4.0
Super-twisting sliding mode control for enhanced performance of grid-connected PV systems with H-bridge multilevel inverter
This paper presents an enhanced control strategy for a grid-connected photovoltaic (PV) system employing a novel H-bridge multilevel inverter (MLI). The key contribution of this work lies in replacing the conventional proportional-integral (PI) controller with a super-twisting sliding mode controller (STSMC) for DC-link voltage regulation. Unlike earlier approaches that suffer from slow response, steady-state errors, and limited robustness under varying solar and temperature conditions, the proposed STSMC ensures faster transient response, finite-time convergence, and strong disturbance rejection without the chattering problem of classical sliding mode controllers. Another distinctive aspect of this study is the integration of STSMC with direct model predictive control (DMPC) for grid current regulation, enabling accurate reference current generation and improved synchronization. The novel H-bridge MLI topology further enhances system efficiency by reducing the number of switches while producing a seven-level output with lower total harmonic distortion (THD). Simulation results demonstrate that the proposed strategy achieves superior performance compared to the conventional PI-based system, with improvements in voltage stability, current quality, and reduced THD. These findings confirm the novelty and effectiveness of the proposed control scheme for reliable and efficient PV grid integration.
Copyright (c) 2026 CH. Venkata Amarnadh, T. Vijay Muni, T. Anuradha Devi, Rakesh Teerdala, M. Kiran Kumar, Kambhampati Venkata Govardhan Rao http://creativecommons.org/licenses/by-sa/4.0
Self tuning of output scaling factor for type-2 interval fuzzy controllers
In this study presents a self-tuning type-2 fuzzy logic controller framework, which operates on the principle of continuously adjusting the controller structure by modifying the controller gain (scaling factor) as a function of the error and its rate of change, in order to achieve optimal control performance. The proposed structure is both simple and robust, with real-time gain adaptation facilitated by two type-2 fuzzy systems; the first one containing the rules of control task for speed regulation, and the second one containing the rules for the adaptation of the scaling factor. Both systems have the same inputs error and its variation. This work specifically focuses on tuning the output scaling factor, which is considered equivalent to the controller gain. The effectiveness of the proposed approach is evaluated through its application to the control of an induction machine, a system known for its complexity and strong nonlinearity. Simulation results demonstrate that the fuzzy controller significantly enhances performance, even under challenging operating conditions such as low-speed regimes.
Copyright (c) 2026 Mouna Ghanai, Kheireddine Chafaa, Ali Medjghou, Nadia Bounouara http://creativecommons.org/licenses/by-sa/4.0
Simulation and comparison of trapezoidal triangle carrier signal with different reference signal for 1500 V DC bus 3 level ANPC inverter
High voltage application to generate staircase output to reduce the total harmonic distortion (THD), the multilevel topologies gaining more and more attractions, and new topologies have been developed. This paper discuss about the advantage of active neutral point clamp (ANPC) topology over neutral point clamp (NPC), flying capacitor neutral point clamp (FCNPC) and T-type neutral point clamp (TNPC) topologies are discussed when it used for DC bus voltage of 1500 V. For ANPC topology several PWM techniques are used to calculate the total harmonic distortion, including phase opposition pulse width modulation (PODPWM), phase disposition pulse width modulation (PDPWM), and alternative phase opposition disposition pulse width modulation (APODPWM), phase shifted pulse width modulation (PSPWM), bus clamping PWM (BCPWM), trapezoidal triangle PWM (TRPWM), third harmonic injected PWM (THIPWM), and sinusoidal PWM (SPWM), three-phase sinusoidal signals with a 13th harmonic signal (THISDPWM). Also, the parasitic inductance model of ANPC topology is discussed. To use 1200 V switching device the most efficient PWM technique for a 1500 V DC bus, 3 phase 3 level ANPC inverter is determined by comparing the RMS value of phase voltage, THD, and peak voltage across the switching device. PSIM has been used to simulate a 3 level inverter using various PWM techniques.
Copyright (c) 2026 Miteshkumar N. Priyadarshi, Sandeep Chakravorty http://creativecommons.org/licenses/by-sa/4.0
Optimizing real-time energy control in hybrid low-voltage microgrids using a multi-agent approach
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.
Copyright (c) 2026 Doha El Hafiane, Abdelmounime El Magri, Ilyass El Myasse, Adil Mansouri, Rachid Lajouad http://creativecommons.org/licenses/by-sa/4.0
Optimized resonant capacitor and switching frequency for high-efficiency wireless power transfer in E-bikes using CST Studio Suite
Wireless power transfer (WPT) is increasingly adopted for E-bike charging; however, its performance is often constrained by inaccurate resonant tuning, inefficient capacitor selection, and improper switching-frequency operation, which lead to significant power loss and reduced transfer efficiency. This study addresses these limitations by formulating an optimized design methodology for selecting resonant capacitors and inverter switching frequency to achieve high-efficiency energy transfer. A 40-mm air gap between the transmitter and receiver coils is modeled using CST Studio Suite, where a 3D electromagnetic circuit co-simulation framework is applied to evaluate mutual inductance, resonant behavior, magnetic-field distribution, and S-parameter characteristics. Parametric sweeps combined with a convergence-based optimization algorithm identify the optimal resonant operating point, yielding a peak resonant frequency of 38.1 kHz, a maximum simulated transfer efficiency of 99%, and a deep reflection coefficient of -21.77 dB. The optimized configuration also demonstrates stable voltage and field distribution at resonance, confirming effective impedance matching. The main contributions of this work include: i) establishing a unified EM–circuit optimization workflow for determining resonant capacitance and switching frequency, ii) providing quantitative resonance parameters and performance indicators suitable for compact E-bike WPT systems, and iii) integrating mathematical modelling to validate CST-based predictions and ensure theoretical consistency. The proposed approach significantly enhances design accuracy and efficiency, offering a scalable and high-performance solution for next-generation low-power electric vehicle (EV) and E-bike wireless charging applications.
Copyright (c) 2026 Wan Muhamad Hakimi Wan Bunyamin, Rahimi Baharom http://creativecommons.org/licenses/by-sa/4.0
Performance degradation analysis of induction motors using Simulink and hybrid method
Voltage unbalance faults (VUF) have a significant adverse impact on the performance and operational lifespan of induction motors. This paper presents a hybrid method that integrates multi-sensor analysis to evaluate induction motor behavior under different levels of electrical fault conditions. The research methodology comprises the development of a three-phase induction motor model in MATLAB/Simulink, combined with experimental monitoring of current, voltage, rotational speed, acoustic signals, and torque. The collected data are analyzed using linear regression to quantify performance degradation. The results indicate that increasing fault severity correlates with reductions in motor efficiency and operational stability. Furthermore, a hybrid technique incorporating modulation analysis of acoustic signals derived from vibration and resonance is proposed to improve the accuracy of efficiency and loss estimation. This approach outperforms conventional methods and demonstrates strong potential for industrial applications, as it effectively mitigates the negative effects of voltage supply faults.
Copyright (c) 2026 Kamrai Janprom, Sittadach Morkmechai, Natchanun Prainetr, Supachai Prainetr http://creativecommons.org/licenses/by-sa/4.0
Techno-economic assessment of gas engine power plants penetration in a power grid
This paper presents a techno-economic assessment of integrating engine power plants into a power grid, using the snake optimization (SO) algorithm to solve the multi-objective optimal power flow (OPF) problem. The study focuses on four key objectives: minimizing fuel costs, reducing voltage deviation, enhancing voltage stability, and minimizing active power losses. Simulations conducted on the 38-bus of Manokwari grid system demonstrate that the SO algorithm significantly improved performance in all areas. Fuel costs were reduced to 2.003 million USD/h while maintaining a stable voltage profile. Voltage deviation was reduced to 0.5577 p.u., ensuring better voltage consistency across the grid. Voltage stability was enhanced with a minimized Lmax value of 0.0200 p.u., and active power losses were reduced to 0.3423 MW, reflecting a notable increase in system efficiency. These findings demonstrate the effectiveness of integrating gas engine power plants, which led to noticeable improvements in operational efficiency and grid stability.
Copyright (c) 2026 Adelhard Beni Rehiara, Frederik Haryanto Sumbung http://creativecommons.org/licenses/by-sa/4.0
Utilization of BSA optimized cascade controller in a renewable energy-based AGC systems
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.
Copyright (c) 2026 Rambabu Kasukurthi, R. Srinu Naik http://creativecommons.org/licenses/by-sa/4.0
Mathematical modelling and automated control strategies for sugarcane crushing system of sugar factory
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.
Copyright (c) 2026 Govind Singh Jethi, Sandeep Sunori, Surya Kant, Pradeep Juneja http://creativecommons.org/licenses/by-sa/4.0
Regenerative braking with battery management system in E-bike
Energy neither be created nor be destroyed, but it can be transformed into other forms as per the law of conservation of energy. This information is epitomized by the regenerative braking system (RBS), which transforms kinetic energy into mechanical energy, thus recuperating waste energy into mechanical energy and making it beneficial. The regenerative braking have significant impact in electric vehicle technology due to the contemporary energy challenges and dwindling resources. Regenerative braking involves apprehending the lost kinetic energy during braking and converting it into a storable or instantly usable form. The recuperated kinetic energy can be reintegrated into vehicle’s power system or stored for further use, often in a battery, especially lithium-ion batteries which are managed by a battery management system (BMS) to ensure optimal performance and longevity. The utilization of various sensors by BMS to monitor parameters such as temperature, current, and voltage, entitling it to assess the battery’s health and determine its state of charge and discharge. Additionally, the BMS protects the battery against cavernous discharge and over-voltage, which can result from rapid discharging and charging currents, thereby optimizing the utilization of battery energy. In this article, the design of an electrical regenerative braking system with a battery management system in an electric bicycle (E-bike) applications are presented. The results show that the system works well in both battery-operated and regenerative modes. When in regenerative mode, the voltage and current stay within the specified range and are suitable for charging batteries. On the other hand, during regular operation, the increase in energy consumption is matched with the battery mode mileage.
Copyright (c) 2026 B. P. Divyashree, G. Lakith, H. N. Sukanya, Nagaling M. Gurav, Neeli Mallikarjuna, Unnam Anil http://creativecommons.org/licenses/by-sa/4.0
Fuzzy logic-based MPPT control for solar PV fed three-phase induction motor drive in water pumping applications
The reliance on traditional energy sources for the agricultural water pumping framework leads to higher operational costs and environmental concerns. The partial shadings severely reduce the solar photovoltaic based water pumping framework efficacy. Owing to partial shading conditions, the output power of the photovoltaic array degrades, which reduces the water pumping output. Therefore, this article presents a low-cost solar photovoltaic fed three phase induction motor driven system, which helps for the rural water pump application. Here, maximum power point tracking of solar photovoltaic panel is done by fuzzy logic. Usually, the solar panel gives direct current power is stored in recharging battery and then setups and acts as source for voltage source inverter in the standalone model. A novel single step battery-low power transformation is used by developing a fuzzy maximum power track with buck-boost chopper that makes the total setup cost to reduced significantly. Mamdani fuzzy system is selected for maximum power point tracking (MPPT) controller because it has more intuitive and easier to understand If-Then rule bases. The suggested method is worked as a model having photovoltaic array, maximum power tracking with the buck-boost chopper, voltage source inverter, and three phase induction motor drive. The inverter is used sinusoidal pulse width modulation as the control algorithm. To check the simulation results the total set up is carried out in MATLAB software. The test results observed that the proposed prototype is useful. It’s efficacy i.e., 75% with in response time (1 sec) for different insulations and temperatures.
Copyright (c) 2026 K. Chitanya, Arjyadhara Pradhan, Babita Panda http://creativecommons.org/licenses/by-sa/4.0
Intelligent gear shifting in electric and hybrid vehicles: a CAN controller-based approach using SOC%
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.
Copyright (c) 2026 Kalagotla Chenchireddy, Naresh Jella, Vadthya Jagan, R. Naveena Bhargavi, Shabbier Ahmed Sydu, Nunavath Praveen http://creativecommons.org/licenses/by-sa/4.0
Adaptive P&O algorithm for fast and acurate maximum power point tracking for PV system
In this study, we proposed an adaptive perturb and observe (P&O) algorithm designed for efficient maximum power point tracking (MPPT) in photovoltaic (PV) systems. This method addresses key challenges in solar energy systems, including variability in solar irradiation and partial shading conditions. The proposed method introduced a dynamic and adaptive in adjusting the step size of the P&O as it nears the maximum power point (MPP), enhancing tracking precision and reducing energy losses. To show the ability of the proposed, we compared it with the conventional P&O and GWO & P&O. The proposed adaptive P&O MPPT algorithm consistently maintains near ideal tracking efficiency of ≈99.7% across various irradiance scenarios, significantly outperforming conventional P&O, which drops to 74.45% under partial shading. Overall, it achieves an average efficiency of 99.71%, surpassing hybrid P&O GWO (99.52%) and conventional P&O (91.30%), demonstrating superior reliability and energy harvesting performance. The results indicated that the proposed could reduce power deviations and obtain greater accuracy in detecting MPP. The study confirms the method's potential for optimizing energy extraction and suggests further refinement for broader applicability. This advancement represents a significant step in enhancing the reliability and efficiency of PV systems in both grid-connected and off-grid applications.
Copyright (c) 2026 Fathurrahman, Rika Sri Utami, Akhyar, Khairun Saddami http://creativecommons.org/licenses/by-sa/4.0
Hourly scheduling of thermal units utilizing an innovative hybrid approach
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.
Copyright (c) 2026 Vempalle Rafi, Shaik Hussain Vali, Sadhu Radha Krishna, Uppuluri Suryavalli, M. Rajesh, Sayapogu Prateepkumar http://creativecommons.org/licenses/by-sa/4.0
A novel single-stage high-voltage gain DC-DC boost converter for on-board PEV charging system
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.
Copyright (c) 2026 Motepalli Siva Rama Ganesh, S. Sasikumar, B. Suresh Babu http://creativecommons.org/licenses/by-sa/4.0
A newly proposed IVCVR controlled IUPQC device for PQ enhancement in multi-feeder distribution networks
Nowadays, the greater relevance of power-quality has being received substantial attention in multi-feeder distribution system due to increased usage of critical non-linear power-electronic loads in many applications. These loads proliferates the quality-power and it can degrade the voltage and current quality in multi-feeder networks from the utility-grid code specifications. Numerous custom-power compensation devices are accessible to mitigate corresponding voltage/current relevant PQ concerns, resulting that the multi-feeder networks are maintained as fundamentally strong, sinusoidal wave-shape, essentially balanced, linear/stable in nature. Amid of several custom-power compensation devices, the interline-UPQC is the most significant for enhancing both voltage/current waveforms in utility-grid integrated multi-feeder distribution system by employing suitable control algorithms. It is noted that, the newly proposed Integrated Voltage-Current Vector Reference (IVCVR) control algorithm eliminates the various technical issues in conventional schemes. In this work, a novel IVCVR algorithm controlled IUPQC device has been proposed for PQ enhancement and also maintaining flexible power-flow between the multi-feeders. The operation and performance of newly proposed IVCVR algorithm controlled IUPQC device has been investigated under definite PQ problems by using Matlab/Simulink software-computing tool. The extracted simulation results are highlighted with feasible interpretations complying with IEEE-519/2022 standards.
Copyright (c) 2026 CH. V. Ramachandra Rao, M. Arun, B. Suresh Babu http://creativecommons.org/licenses/by-sa/4.0
Hydroelectric power generator using vertical axis turbine with adaptive blades
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.
Copyright (c) 2026 Rizki Nurilyas Ahmad, Soraya Komala Firdaus, Mohammad Nasrul Mubin, Hasyim Asy'ari, Tindyo Prasetyo, Iqbal Reza Pradana http://creativecommons.org/licenses/by-sa/4.0
Analyzing the ability of capacitor energy in a modular multilevel converter to support inertia in an AC system
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.
Copyright (c) 2026 Dunya Sh. Wais, Huda A. Abbood http://creativecommons.org/licenses/by-sa/4.0
Comparison of differential evolution optimization technique with other techniques in solving multi-objective optimal power flow
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.
Copyright (c) 2026 Vineeta S. Chauhan, Jaydeep Chakravorty, Siddharthsingh K. Chauhan http://creativecommons.org/licenses/by-sa/4.0
Performance evaluation of several basic types of DC-DC converters for small-scale wind turbine connected to isolated load
Small-scale wind turbines (SSWT) encounter a primary issue in power extraction, making the integration of DC-DC converters with maximum power point tracking (MPPT) crucial for performance enhancement. This study focuses on an evaluation of basic DC-DC converters, which offer many benefits when applied to SSWT systems. By considering efficiency, suitability, and electrical stress aspects, six DC-DC converter topologies consisting of boost, buck-boost, SEPIC, Cuk, zeta, and Luo, were tested under perturb and observe (P&O) and incremental conductance (INC) MPPT strategies. A 6.5-kW SSWT system, including a DC-DC converter and MPPT was designed in detail using PSIM. The results show that boost converter demonstrated the best overall performance in terms of power extraction, efficiency, and stress reduction, although its operation was limited to a narrower wind speed range. The zeta converter achieved efficiency and power extraction comparable to the boost converter with stable operation over a broader wind speed range, while the buck–boost converter offered step-up/step-down capability but experienced higher voltage and current stress. The SEPIC and Cuk converters showed low overall performance compared to others, whereas the Luo converter was better suited for low-wind-speed conditions.
Copyright (c) 2026 Rizki Mendung Ariefianto, Rini Nur Hasanah, Hadi Suyono, Tri Nurwati, Eduard Muljadi, Hazlie Mokhlis http://creativecommons.org/licenses/by-sa/4.0
Predictive modeling and optimization of paper mill using hybrid machine learning techniques
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.
Copyright (c) 2026 Abhijit Singh Bhakuni, Sandeep Kumar Sunori, Pradeep Juneja http://creativecommons.org/licenses/by-sa/4.0
Moth flame optimization based super twisting sliding mode MPPT controller for grid connected PV system
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.
Copyright (c) 2026 Ujwala Gajula, Gouthami Eragamreddy, N. Malla Reddy, Remala Geshma Kumari, Veeranjaneyulu Gopu http://creativecommons.org/licenses/by-sa/4.0
Sliding mode control of a solar powered switched-inductor based quadratic DC-DC converter for sustainable EV battery charging application
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.
Copyright (c) 2026 Jawahar Marimuthu, Edward Rajan Samuel Nadar http://creativecommons.org/licenses/by-sa/4.0
Energy-aware dynamic adjustment integrated kookaburra optimization based efficient routing in WSN
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.
Copyright (c) 2026 Shobanbabu R. Jaganathan, R. Sathya, R. Karthikeyan http://creativecommons.org/licenses/by-sa/4.0
Voltage stress mitigation in high-gain DC-DC converters via dual Z-source DC-DC converter
This paper presents a novel dual Z-source DC-DC converter designed to address the limitations of conventional high step-up converters used in renewable energy applications such as solar photovoltaic systems and fuel cells. Traditional boost and impedance-source converters often suffer from high voltage stress, low efficiency at higher power levels, and complex multi-stage configurations. To overcome these challenges, the proposed topology integrates a hybrid structure comprising symmetrical inductors and capacitors, enabling high voltage gain at reduced duty cycles while minimizing component stress. The converter is analytically modelled and evaluated under continuous conduction mode, and its performance is verified through MATLAB/Simulink simulations and experimental validation using a hardware prototype. The results demonstrate that the proposed converter achieves a voltage gain of up to 10× with a duty cycle below 0.5, while maintaining efficiency above 95% and significantly reducing voltage stress across switching devices. Compared to existing high step-up converters, the proposed design offers improved efficiency, reduced component count, and enhanced reliability. These features make it a promising solution for efficient and sustainable energy conversion in modern renewable energy systems.
Copyright (c) 2026 Jawahar Marimuthu, Arockiaraj Sesaiya, Bhavani Ramachandran, Ramya Hyacinth Lourdusamy http://creativecommons.org/licenses/by-sa/4.0
Stochastic planning for feeding a green hydrogen plant into an isolated network
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.
Copyright (c) 2026 Michael Salcedo, Mario A. Rios http://creativecommons.org/licenses/by-sa/4.0
Impact of synchronous condensers on voltage stability in systems with high renewable energy penetration
The rapid integration of renewable energy sources (RES) poses significant challenges to power system reliability, particularly regarding voltage stability and reduced loadability margins. This study investigates the impact of synchronous condensers as a mitigation strategy to enhance stability in grids with high renewable penetration. The research objective is to evaluate how these devices influence loadability margins while considering the inherent stochastic nature of RES. The methodology employs PV curves for static voltage stability assessment, utilizing the 2m+1 point estimate method (PEM) to model uncertainty with high computational efficiency. This approach allows for the calculation of statistical indicators, including mean values, standard deviations, and confidence intervals for loadability margins. Simulations were conducted on the IEEE reliability test system (RTS) using NEPLAN360 software. The results demonstrate that the deployment of synchronous condensers (SCs) significantly improves voltage stability by increasing load margins and reducing the standard deviation of uncertainty. Conclusions indicate that these devices are effective reactive power compensators that provide a more robust operational environment against RES variability. Future research will focus on the optimal sizing and placement of these compensators to further maximize grid security.
Copyright (c) 2026 Juan Esteban Rodríguez Quiroga, Mario A. Rios http://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of PM6:L8-BO organic and inverted organic solar cell
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.
Copyright (c) 2026 Karthika Krishnakumar, Ashish Grover, Pardeep Kumar http://creativecommons.org/licenses/by-sa/4.0
Smart IoT-based temperature-controlled cooling system for solar panels using Arduino
The efficiency of solar photovoltaic panels declines significantly as their surface temperature increases beyond optimal levels. This paper presents a smart, temperature-controlled cooling system based on an Arduino UNO microcontroller to enhance solar panel performance by mitigating overheating. The system integrates a DS18B20 temperature sensor and a moisture sensor to monitor real-time environmental conditions. When the temperature exceeds a defined threshold, the Arduino activates a CPU fan and water pump to dissipate heat effectively. Experimental testing demonstrated an efficiency improvement of approximately 10% to 12% during peak solar conditions. A hysteresis logic-based system with autonomous control is used to control the amount of energy and water utilized, by only cooling when required. An LCD screen displays real-time information locally on-site, while an ESP8266 WiFi module sends information to a "cloud" so that remote monitoring can occur through the ThingSpeak cloud service. The entire system operates entirely from solar energy and is capable of being operated off-the-grid as well as being environmentally friendly. Due to its low cost, modularity, and energy efficiency, this smart cooling solution provides a viable solution for rural areas or areas with limited resources to enhance the performance of photovoltaic systems.
Copyright (c) 2026 Mula Sreenivasa Reddy, Kondragunta Rama Krishnaiah, Anjali Devi Gorla, Anantha Sravanthi Peddinti, Sanam Nagendram, Mohammad Najumunnisa, Bodapati Venkata Rajanna, Shaik Hasane Ahammad, Gongati Pandu Ranga Reddy http://creativecommons.org/licenses/by-sa/4.0
Design to optimize the location, number, and performance of dynamic voltage restorers using artificial neural networks
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.
Copyright (c) 2026 Yulianta Siregar, Faizzufar Taqy, Mohd Najib Mohd Hussain, Hafizh Prihtiadi, Muldi Yuhendri http://creativecommons.org/licenses/by-sa/4.0
Adaptive telematics integration for enhanced EV fleet management and data acquisition
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.
Copyright (c) 2026 Kavitha Kumaraswamy, Pasumarthi Usha, S. Ashok Kumar, Deekshitha Arasa, Suganthi Neelagiri http://creativecommons.org/licenses/by-sa/4.0
Wind direction based aggregation of wind power plants under exact wind speeds
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.
Copyright (c) 2026 Ali M. S. Al-Bayati, Huda Hamza Abdulkhudhur http://creativecommons.org/licenses/by-sa/4.0
Sustainability and strategic development of biogas generated from tofu manufacturing wastewater
Tofu is a widely consumed soy-based food in Indonesia, and its liquid waste is utilized for biogas in Sambak Village, addressing both renewable energy and waste management issues. However, the system's sustainability faces challenges. This research aimed to assess the current sustainability status of the tofu-wastewater-based biogas system and formulate strategic measures to optimize its long-term continuity. Sustainability was analyzed across five dimensions (ecological, economic, social, technological, and institutional) with multidimensional scaling (MDS) method, while strategies were formulated using SWOT analysis. The results showed an overall moderately sustainable system with an index score of 74.15. The ecological, economic, and social dimensions were rated very sustainable, while the technological dimension was quite sustainable, and the institutional dimension was less sustainable. The top priority strategy identified is the development and innovation of biogas installations. While biogas offers significant environmental and social economic benefits, sustainability is hindered by limited biogas volume and weak institutional management. Therefore, guidance, regular monitoring involving all stakeholders, and future supply-demand forecasting are crucial for its long-term viability.
Copyright (c) 2026 Hashfi Hawali Abdul Matin, Sapta Suhardono, Prabang Setyono, Glora Ramadhani, Yoyon Wahyono, Budiyono http://creativecommons.org/licenses/by-sa/4.0
Enhancing torque performance in electric four-wheel drive systems using fuzzy GPC
This paper presents a robust supervisory control strategy for speed regulation in a four-wheel-drive electric vehicle (EV) equipped with in-wheel induction motors. A hybrid control architecture is developed by combining fuzzy logic control (FLC) and generalized predictive control (GPC), with an intelligent switching mechanism that dynamically allocates control authority based on real-time operating conditions. FLC is employed to manage transient phases such as acceleration and deceleration, while GPC ensures optimal performance during steady-state operation. The proposed control system is modeled and validated in the MATLAB/Simulink environment. Simulation results demonstrate that the hybrid controller achieves a 27% improvement in transient response, a 15% reduction in steady-state speed fluctuations, and a 19% decrease in energy consumption under urban driving conditions. Furthermore, the controller maintains reliable performance under parameter variations of up to 25% and road gradients of up to 15%. Compared to standalone FLC and GPC controllers, the hybrid approach improves transient speed recovery by 35% and reduces steady-state error by 22%. Overall, this hybrid FLC-GPC strategy effectively addresses key challenges in EV control, such as system nonlinearity, parameter uncertainty, and external disturbances, while ensuring high dynamic responsiveness, steady-state precision, and energy efficiency. These results highlight the potential of the proposed method for future intelligent and autonomous electric mobility systems.
Copyright (c) 2026 Djamila Allali, Youssef Mouloudi, Abdeldjebar Hazzab, Najia Allali http://creativecommons.org/licenses/by-sa/4.0
Rule-based energy management strategies for a hybrid microgrid using grey wolf optimizer
This study utilizes grid-connected microgrids using photovoltaics (PVs) and wind turbines (WTs) in a residential system. For improved reliability, the system uses battery storage and diesel generators (Dgen). The proposed system uses supervisory controllers (as a rule-based energy management system) for energy management strategy implementations. The essence of using the grey wolf optimizer (GWO) is to strategize the rule-based energy management system in the proposed microgrid operations. The primary objectives are to achieve a low levelized cost of energy (LCOE) and determine the optimal number of microgrid components. The performance of the GWO is compared with three other optimization algorithms, namely, antlion optimizer (ALO), particle swarm optimizer (PSO), and cuckoo search algorithm (CSA), for benchmarking purposes. The findings indicate that the proposed GWO supersedes ALO, PSO, and CSO in energy cost reduction by 30.3% (0.0448 $/kWh), 65.6% (0.0971 $/kWh), and 120% (0.1774 $/kWh), respectively. The suggested algorithm selects the optimum number of the system’s components, which is 46 PV modules, 30 wind turbines, and 10 units of batteries. An improved GWO-based algorithm based on hybridization with gradient descent algorithms is envisaged to implement a customer-centered energy management that can ensure customer satisfaction and further reduce energy cost.
Copyright (c) 2026 Sarmid Shakir Abdulsattar, Chee Wei Tan, Shahrin Ayob, Yasir Shakir Abdulsattar, Ahmed Tijjani Dahiru, Chin Kim Gan, Kwan Yiew Lau http://creativecommons.org/licenses/by-sa/4.0
Advanced strategy for energy management and voltage stability in microgrid-a review
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.
Copyright (c) 2026 Aswathi Ravindran, B. Rubini http://creativecommons.org/licenses/by-sa/4.0
Comparative performance analysis of MPPT algorithms for wind power generation: P&O, INC, and TSR methods
Wind energy has great potential, especially in areas with high wind speeds such as Southeast Aceh. However, wind speed fluctuations reduce turbine efficiency, necessitating maximum power point tracking (MPPT) for optimization. This study compared three MPPT methods perturb and observe (P&O), incremental conductance (INC), and tip speed ratio (TSR) to identify the most effective technique. Using MATLAB Simulink, simulations were conducted with wind speed data from Southeast Aceh and a DC-DC boost converter. Results showed the P&O method performed best, producing 847.83 W at 10 m/s, compared to 702.40 W for INC and 324.35 W for TSR. P&O also achieved the highest current output, reaching 16.45 A, while INC and TSR produced 13.66 A and 6.34 A, respectively. At lower wind speeds, P&O continued to outperform the other methods. This study concludes that the P&O method is the most effective method to improve the efficiency of wind turbines in Southeast Aceh, while INC shows moderate performance and TSR is the least effective method due to fluctuating wind speeds in a short time, so that TSR cannot maintain its maximum value. Therefore, P&O is recommended as the optimal MPPT technique for wind power plants in this region.
Copyright (c) 2026 Muhammad Aulia Desky, Yulianta Siregar, Maksum Pinem http://creativecommons.org/licenses/by-sa/4.0
Technological and philosophical perspectives on photovoltaic electric bicycles for sustainable mobility
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.
Copyright (c) 2026 Endang Susanti, Azriyenni Azhari Zakri, Antonius Rajagukguk http://creativecommons.org/licenses/by-sa/4.0
Development of groundwater level sensor for internet of things-based peatland fire monitoring
Monitoring groundwater levels in peatlands is crucial for mitigating forest and land fires, which frequently occur in tropical regions. Current peatland groundwater level monitoring systems generally have limitations in their sensing range. Additionally, the lack of electricity and internet infrastructure around the monitoring sites presents another challenge. To address these issues, in this study, we developed a groundwater level sensor based on a float integrated with an optocoupler as a rotary encoder to monitor changes in water levels in real-time. The system is connected to an internet of things (IoT) platform through a LoRa communication module, powered by solar panels, enabling continuous wireless data transmission over a large range. To overcome the sensing range limitation, we developed a rotary encoder with a pulley driven by a long rope. Testing was conducted using transparent pipes with varying water levels to evaluate the sensor's performance. Experimental results showed that the sensor could measure water levels with a resolution of ±3.33 mm. This research provides a technique for measuring groundwater levels with an extended measurement range. Additionally, it produces a peatland fire risk monitoring system based on groundwater level parameters. It is expected can support peatland fire mitigation efforts more efficiently and effectively.
Copyright (c) 2026 Abdul Muid, Nina Siti Aminah, Maman Budiman, Mitra Djamal http://creativecommons.org/licenses/by-sa/4.0
Analysis of CCS implementation in Indonesia’s coal fired power plants, economic optimization, and potential impact on Java-Bali grid for future decarbonization
This study aims to evaluate impact of retrofitting carbon capture and storage (CCS) technology on coal fired power plants (CFPP) in Indonesia. Using a representative 3×330 MW CFPP, the integration of CCS increases the levelized cost of electricity (LCoE) to 124 USD/MWh. Key cost components include CO₂ capture (21.7%), energy penalty from steam extraction (18.5%), and CO₂ transport and injection (16.7%). Sensitivity analysis indicates that CCS becomes financially viable under a high carbon cap (0.9 tCO₂/MWh) and a carbon tax of 76 USD/tCO₂. Meanwhile, International carbon markets offer a potential revenue at 75 USD/tCO₂ can fully offset CCS costs. Additionally, CAPEX grants can reduce LCoE to 12.4%, serving to mitigate upfront investment for CCS deployment. Within the Java-Bali grid, CFPP account for 58.8% of the generation mix with 41% aged 10-20 years using predominantly subcritical technology while 28% are over 20 years old and follow natural retirement being replaced by renewable energy. CCS retrofitting is more economically and technically viable for mid aged plants with newer technologies and lower emission intensities, supporting grid stability with limited renewable base load availability. This strategy also serves as a transitional pathway toward long term renewable integration until the LCoE of PV+BESS falls below 50 USD/MWh.
Copyright (c) 2026 Anggit Raksajati, Sanggono Adisasmito, Veri Hendrayawan http://creativecommons.org/licenses/by-sa/4.0
Investigation of the photoluminescence properties of quantum dots using theoretical simulation
This study investigates the optical behavior of CdSe quantum dots, a class of semiconductor nanomaterials widely studied for light-emitting, photovoltaic, and bioimaging applications owing to their size-dependent electronic structure. The objective is to clarify the relationship between quantum dot size, size distribution, and emission characteristics through experimental and simulated optical spectra. UV-Vis absorption, photoluminescence, and simulated PL spectra were analyzed for CdSe quantum dots excited at 325 nm. The experimental PL spectrum exhibits a single and narrow emission band assigned to the 1Se → 1Sh transition, which is blue-shifted compared with bulk CdSe, confirming strong quantum confinement in 2-3 nm particles with a very narrow size distribution of less than 1%. A large Stokes shift of 0.93 eV is observed, attributed to confinement effects and surface-related states. Simulated photoluminescence (PL) spectra for 3-6 nm quantum dots show progressive red-shifting and spectral broadening with increasing particle size, while smaller quantum dots display stronger PL intensity due to enhanced confinement and more efficient radiative recombination. Parameter analysis further reveals that size deviation and linewidth broaden emission and reduce intensity without changing the peak wavelength. These findings provide useful guidance for optimizing CdSe quantum dots for QLEDs, bioimaging, and broadband optoelectronic devices.
Copyright (c) 2026 Le Doan Duy, Le Xuan Thuy http://creativecommons.org/licenses/by-sa/4.0
Surface passivation-induced enhancement of light absorption in photoanodes for quantum dot-based solar cells
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.
Copyright (c) 2026 Ho Minh Trung, Le Xuan Thuy http://creativecommons.org/licenses/by-sa/4.0