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Dual mode control of an integrated on-board charger powered BLDC drive

10.11591/ijpeds.v17.i2.pp1058-1068
Caroline Ann Sam , Varghese Jegathesan
The high adoption of electric vehicles in transportation has created a demand for compact, efficient, and cost-effective charging solutions for them. Conventional onboard chargers are often bulky, which adds to the overall cost of the drive system, whereas off-board charging infrastructure remains limited. In order to address these issues, this work illustrates the design and modelling of an active power factor corrected integrated onboard charger which gets reconfigured from the electric vehicle drive train components. The proposed circuit setup is designed to work in dual mode, i.e., in the role of a DC-DC converter while charging the vehicle battery and as a three-phase inverter while driving the vehicle. The front-end power factor correction circuit, in addition to the reconfigured DC-DC converter, charges the 24 V, 20 Ah lead acid battery under constant current constant voltage (CC-CV) mode, achieving a power factor close to unity. Modelling and control of the proposed 200 W reconfigurable converter-fed 24 V, 180 W brushless direct current (BLDC) drive is validated using MATLAB/ Simulink Software. Simulation results demonstrate a power factor of 0.996 in grid-connected operation with a total harmonic distortion (THD) of 4.96%. The proposed architecture achieves a compact structure with only 8 switches enabling charging, propulsion and regenerative braking operation. The proposed converter thus contributes to a cost-effective electric vehicle and provides the scope of future extension to vehicle to home (V2H), vehicle to load (V2L), and vehicle to vehicle (V2V) applications as well.
Volume: 17
Issue: 2
Page: 1058-1068
Publish at: 2026-06-01

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

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

An enhanced hybrid deep learning-quantum variational classifier framework for large-scale data analytics

10.11591/ijpeds.v17.i2.pp1522-1532
Yadlapti Suresh , Venu Gopal Gaddam , Challa Naga Venkata Jyothirmai , Rokkam Veera Venkata Nagendra Bheema Rao , Sreenivasulu Bolla , Ankala Radhika
The rapid expansion of clinical data in modern healthcare requires analytical systems capable of uncovering intricate patterns and supporting accurate diagnostic decisions. Quantum machine learning (QML) offers significant potential for modeling higher-order feature interactions and accelerating computation beyond classical approaches. This paper introduces an improved hybrid architecture that fuses an inception-based attentional VGG (IAV) network with a quantum variational classifier (QVC) constructed using parameterized quantum circuits (PQCs). The framework begins with min-max normalization to stabilize heterogeneous clinical attributes and enhance training convergence. Deep discriminative features are then extracted through the IAV model, followed by quantum-driven classification using variational layers optimized by classical routines. The MIMIC-III clinical dataset is employed to validate the proposed system on large-scale healthcare records. Performance is measured using accuracy, precision, recall, and F1-score. The enhanced hybrid model achieves 97.28% accuracy, 97.16% precision, 96.65% recall, and a 97.38% F1-score, surpassing established methods including support vector machine (SVM) (89.23%), quantum support vector machine (QSVM) (90.13%), and QVKSVM (97.34%). The findings confirm that integrating deep learning with quantum variational optimization strengthens scalability, reduces computational overhead, and establishes a powerful foundation for next-generation healthcare analytics.
Volume: 17
Issue: 2
Page: 1522-1532
Publish at: 2026-06-01

Advanced soft-switching high-gain Re Boost Luo converter for enhanced efficiency in photovoltaic systems

10.11591/ijpeds.v17.i2.pp1177-1187
Vendoti Suresh , Dondapati Ravi Kishore , T. Vijay Muni , P. Hari Krishna Prasad , Pydi Bala Krishna , A. V. G. A. Marthanda
This work presents an innovative approach to improving efficiency and performance in photovoltaic (PV) systems through the development of a soft-switching high-gain Re Boost Luo converter. This converter integrates advanced soft-switching techniques to minimize switching losses, thereby enhancing overall system efficiency, which is crucial for applications requiring substantial voltage amplification from PV sources. The Re Boost Luo converter, with its inherent high-gain capability, facilitates superior voltage conversion ratios, enabling optimal energy extraction from PV panels across varying environmental conditions. The presented converter's design focuses on reducing electromagnetic interference (EMI) and alleviating stress on switching components, thereby extending their operational lifespan and reliability. Detailed modeling and performance analysis were carried out using the MATLAB/Simulink simulation environment, which allowed for comprehensive evaluation of the converter's functionality. Simulation results confirm that the converter achieves significant improvements in voltage gain, energy conversion efficiency, and system reliability, effectively addressing common challenges associated with high-voltage PV applications. This study underscores the converter's potential to advance renewable energy technologies by providing a robust solution for high-efficiency energy conversion in PV systems.
Volume: 17
Issue: 2
Page: 1177-1187
Publish at: 2026-06-01

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

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

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

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

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

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

Hourly scheduling of thermal units utilizing an innovative hybrid approach

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

Development of groundwater level sensor for internet of things-based peatland fire monitoring

10.11591/ijape.v15.i2.pp915-926
Abdul Muid , Nina Siti Aminah , Maman Budiman , Mitra Djamal
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.
Volume: 15
Issue: 2
Page: 915-926
Publish at: 2026-06-01

Impact of synchronous condensers on voltage stability in systems with high renewable energy penetration

10.11591/ijape.v15.i2.pp760-769
Juan Esteban Rodríguez Quiroga , Mario A. Rios
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.
Volume: 15
Issue: 2
Page: 760-769
Publish at: 2026-06-01

Adaptive P&O algorithm for fast and acurate maximum power point tracking for PV system

10.11591/ijape.v15.i2.pp590-599
Fathurrahman Fathurrahman , Rika Sri Utami , Akhyar Akhyar , Khairun Saddami
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.
Volume: 15
Issue: 2
Page: 590-599
Publish at: 2026-06-01

Enhancing torque performance in electric four-wheel drive systems using fuzzy GPC

10.11591/ijape.v15.i2.pp845-857
Djamila Allali , Youssef Mouloudi , Abdeldjebar Hazzab , Najia Allali
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.
Volume: 15
Issue: 2
Page: 845-857
Publish at: 2026-06-01

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

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

Performance degradation analysis of induction motors using Simulink and hybrid method

10.11591/ijape.v15.i2.pp525-534
Kamrai Janprom , Sittadach Morkmechai , Natchanun Prainetr , Supachai Prainetr
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.
Volume: 15
Issue: 2
Page: 525-534
Publish at: 2026-06-01
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