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Experimental validation of virtual flux concept in direct power control with dynamic performance

10.11591/ijpeds.v16.i4.pp2509-2520
Muhammad Hafeez Mohamed Hariri , Nor Azizah Mohd Yusoff , Muhammad Zaid Aihsan , Tole Sutikno
The virtual-flux direct power control (VFDPC) technique is a sensorless control approach aimed at improving the performance of grid-connected power converters. The approach involves simulating the grid voltage and AC-side inductors similar to an AC motor drive system, a principle deriving from direct torque control (DTC). The basic idea of VFDPC is to indirectly estimate the voltage at the converter's input through the concept of virtual flux, enabling the real-time calculation of instantaneous active and reactive power without necessitating direct voltage measurements. An essential element of the VFDPC approach is the implementation of a lookup table, used as a decision-making tool that identifies the most suitable voltage vector (a particular output state of the converter) in accordance with real-time power conditions. This provides instantaneous and smooth control of power flow, leading to enhanced operational stability. This approach allows for continual optimization of the converter's output, enabling VFDPC to significantly decrease total harmonic distortion (THD) while preserving reliable steady-state and dynamic performance. Experimental validation demonstrates that incorporating real-time feedback into virtual flux estimates improves the precision of voltage prediction and the responsiveness of the power control system. Consequently, VFDPC exhibits enhanced adaptability for various grid and load situations, presenting an appropriate choice for current power systems that demand efficient, reliable, and sensorless operation.
Volume: 16
Issue: 4
Page: 2509-2520
Publish at: 2025-12-01

A comprehensive review of efficient wireless power transfer for electric vehicle charging: advancements, challenges, and future directions

10.11591/ijpeds.v16.i4.pp2156-2169
Md. Ashraf Ali Khan , Kuber Kuber , Yusra Wahab , M. Saad Arif , Shahrin Md. Ayob , Norjulia Mohamad Nordin
Electric vehicles (EVs) have transformed the transportation sector, offering a sustainable alternative to fossil-fuel-powered vehicles. However, their widespread adoption faces challenges such as inadequate charging infrastructure, range anxiety, and concerns about user convenience. Wireless power transfer (WPT) technology provides an efficient, reliable, and user-friendly charging solution that eliminates physical connections, enabling both static and dynamic charging applications. This review explores key components of WPT systems, including wireless charging schemes, compensation circuits, coupling pad structures, and misalignment tolerance, emphasizing their impact on system efficiency and reliability. Findings highlight that WPT can enhance charging convenience, reduce dependence on large battery capacities, and support seamless EV integration into daily life. Additionally, WPT systems improve safety, lower maintenance needs, and create opportunities for autonomous charging. Key advancements in compensation topologies, coupling pad geometries, and misalignment-tolerant capabilities are discussed alongside their role in enhancing power transfer efficiency. By offering insights into the current state-of-the-art and future directions, this paper aims to support the development and deployment of WPT systems, contributing to the global transition toward sustainable transportation.
Volume: 16
Issue: 4
Page: 2156-2169
Publish at: 2025-12-01

Combination circuit of multilevel inverter, matrix converter, and H-bridge

10.11591/ijpeds.v16.i4.pp2476-2490
Akram Mohammed Al-Mahrouk , Nashiren Farzilah Mailah , Mohd Amran Mohd Radzi , Mohd Khair Hassan
In this study, a new integrated circuit design called H-bridge multilevel inverter matrix converter (HMIMC), which combines a multilevel inverter (MI), a matrix converter (MC), and an H-bridge circuit, is developed. It aims to generate a high number of output voltage levels that reduce the component count (CC). The MI step is used to control the positive voltage source, where the output of MI is connected to the input of MC. The MC is used to share the positive input voltage due to output phases, depending on the requirements. Afterward, the H-bridge circuit is used in each phase to select the positive or negative output voltage. The main contribution of this design is that the MI does not need to be repeated thrice to produce a three-phase output voltage. A seven-level (7L) and thirteen-level (13L) of proposed circuit is presented, followed by a new algorithm operation that is used for suitable switching control. Afterward, MATLAB simulation is used to check the operation process, output signals of voltage and current, and total harmonic distortion (THD) results. Then hardware circuit of the proposed system is implemented to verify the design. Lastly, a brief comparison in terms of CC is conducted.
Volume: 16
Issue: 4
Page: 2476-2490
Publish at: 2025-12-01

Development of a PEM fuel cell equivalent circuit model with PINN-based parameter identification

10.11591/ijpeds.v16.i4.pp2804-2818
Ismail Ait Taleb , Zakaria Kourab , Souad Tayane , Mohamed Ennaji , Jaafar Gaber
This paper presents a novel equivalent electrical circuit model for proton exchange membrane fuel cells (PEMFCs) and introduces a physics-informed neural network (PINN) algorithm for parameter identification. The proposed model provides a more accurate representation of the fuel cell’s dynamic behavior while maintaining computational efficiency. Unlike conventional methods, the PINN framework integrates physical constraints with data-driven learning, ensuring physically consistent parameter estimation. To validate its effectiveness, the proposed model is compared with the widely used RC equivalent circuit and a generic PEMFC model. Experimental data from a 1.2 kW PEMFC test bench serve as a benchmark for evaluating the transient and steady-state performance of each modeling approach. Results demonstrate that the proposed circuit, combined with PINN-based identification, yields enhanced accuracy in predicting voltage response under various operating conditions. Additionally, the model exhibits improved adaptability to transient phenomena compared to conventional equivalent circuits. These findings highlight the potential of physics-informed machine learning for advancing fuel cell modeling and control strategies.
Volume: 16
Issue: 4
Page: 2804-2818
Publish at: 2025-12-01

Optimization of solar panel orientation and tracking systems for standalone PV applications

10.11591/ijpeds.v16.i4.pp2721-2730
Singgih Dwi Prasetyo , Yuki Trisnoaji , Misbahul Munir , Meirna Puspita Permatasari , Abram Anggit Mahadi , Marsya Aulia Rizkita , Zainal Arifin
The performance of photovoltaic (PV) systems is greatly influenced by the angle of arrival of sunlight and the geometric orientation of solar panels, especially in tropical regions with the potential for solar energy throughout the year. This study aims to evaluate the effect of tilt angle variation and tracking systems on energy output and performance indicators of standalone PV systems using PVsyst software. The simulation was conducted at the State University of Malang, Indonesia, by comparing four fixed-angle configurations (20°, 40°, 60°, and 80°) as well as a two-axis tracking system. The simulation results showed that the two-axis tracking system produced the highest normalized daily energy production of 6.8 kWh/kWp/day, with a performance ratio (PR) of 77.2% and a solar fraction (SF) of 97.1%, while a fixed configuration with an angle of 80° showed the lowest performance. These findings confirm the importance of selecting optimal panel orientation to maximize the efficiency of PV systems, as well as being the basis for the development of advanced research, such as field-based experiments, integration of adaptive MPPT algorithms, and economic feasibility studies in the application of PV systems in tropical and off-grid regions.
Volume: 16
Issue: 4
Page: 2721-2730
Publish at: 2025-12-01

Predictions of solar power using ensemble machine learning techniques

10.11591/ijpeds.v16.i4.pp2868-2878
Arangarajan Vinayagam , R. Mohandas , R. Jeyabharath , B. S. Mohan , Srinivasan Lakshmanan , C. Bharatiraja
Predicting solar power production accurately is becoming more and more crucial for efficient power management and the grid's integration of renewable energy sources. Using data from an Australian photovoltaic (PV) power station, this study employs a variety of machine learning (ML) ensemble techniques, such as gradient boosting (GB), random forest (RF), and extreme gradient boosting (XGBoost), to forecast solar power production. ML models are developed utilizing pertinent information from electricity and meteorological data in order to forecast solar power. The predictive performance of trained ML models is verified in terms of metrics like mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R2). With higher R2 values and lower error results (MAE and RMSE), XGBoost performs better than GB and RF. Optimizing the hyperparameters of the XGBoost model significantly improves its performance. The tweaked XGBoost model shows a significant improvement in R2 (more than 5% to 10%) and error results (reduced MAE and RMSE by 0.01 to 0.06), when compared to other ensemble approaches. Compared to other ensemble approaches, the tuned XGBoost methodology is more robust and generates more accurate forecasts in solar power.
Volume: 16
Issue: 4
Page: 2868-2878
Publish at: 2025-12-01

Robust sliding mode control of a DFIG based on the SVM strategy

10.11591/ijpeds.v16.i4.pp2711-2720
Ibrahim Yaichi , Kouddad Elhachemi , Aoumri Mohamed
This paper presents a direct power control (DPC) method for a doubly-fed induction generator (DFIG) used in variable-speed wind power systems, combining sliding mode control (SMC) with space vector modulation (SVM). The proposed SMC-based DPC with SVM (SMC-DPC_SVM) achieves decoupled power control through flux orientation, enhancing performance through the robustness of SMC and the precision of SVM. Simulation results demonstrate the effectiveness of this control strategy. The conventional direct power control (C-DPC) approach delivers fast and robust power response, and a comparative analysis between C-DPC and the proposed SMC-DPC_SVM strategy highlights the advantages of the latter. Robustness was evaluated under varying machine parameters, confirming system stability. The proposed control method was implemented and validated using MATLAB/Simulink, achieving a total harmonic distortion (THD) of less than 5%, indicating high-quality power delivery to the electrical grid.
Volume: 16
Issue: 4
Page: 2711-2720
Publish at: 2025-12-01

Numerical and experimental state of identification battery pack lithium-ion

10.11591/ijpeds.v16.i4.pp2623-2633
Dewi Anggraeni , Budi Sudiarto , Eriko Nasemudin Nasser , Wahyudi Hasbi , Yus Natali , Purnomo Sidi Priambodo
Two key indicators of a battery management system (BMS) are the state of charge (SoC) and the state of health (SoH). Accurately estimating SoC is important to prevent potential issues. Additionally, space, computing time, and cost are important factors in hardware development. To address these considerations, the first-order extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) models were selected due to their simpler data pre-processing and better accuracy. The study recommends using the first-order equivalent circuit model (ECM) method in conjunction with the EKF and AEKF algorithms due to their straightforward setup and efficient computational process. Analysis of the charge-discharge cycles shows that the AEKF method consistently outperformed the EKF method regarding SoC accuracy. Moreover, when given different initial SoC values, the AEKF method displayed superior SoC estimation accuracy compared to the EKF method. Moreover, while the accuracy of the EKF is diminished, the error value remains below 2.5% for up to 500 cycles. Additionally, the shorter computing time of the EKF method is a consideration for practical real-world implementation. Furthermore, experiments conducted over 500 cycles revealed that SoH estimation declined from 99.97% to 76.1947%, suggesting that the battery has reached the end of life (EOL) stage.
Volume: 16
Issue: 4
Page: 2623-2633
Publish at: 2025-12-01

Adaptive intelligent PSO-Based MPPT technique for PV systems under dynamic irradiance and partial shading conditions

10.11591/ijpeds.v16.i4.pp2841-2859
Muhammad Gul E. Islam , Mohammad Faridun Naim Tajuddin , Azralmukmin Azmi , Rini Nur Hasanah , Shahrin Md. Ayob , Tole Sutikno
This research introduces an adaptive improved particle swarm optimization (AIPSO) approach for maximum power point tracking (MPPT) approach designed to enhance energy harvesting from photovoltaic (PV) systems under dynamic irradiance conditions. The proposed AIPSO algorithm addresses the challenges associated with traditional MPPT methods, particularly in scenarios characterized by fluctuating solar irradiance, such as step changes and partial shading. By incorporating a robust reinitialization strategy along with updated velocity and position equations, the algorithm demonstrates superior performance in terms of convergence accuracy, tracking speed, and tracking efficiency. This modification enables the algorithm to effectively escape local maxima and explore a wider search space, leading to improved convergence and optimal power point tracking. Furthermore, the adaptive nature of the PSO enhances the algorithm’s ability to respond to real-time changes in environmental conditions, making it particularly suitable for large- scale PV systems subjected to varying atmospheric factors. Here, “adaptive” denotes coefficient scheduling (C3) and a re-initialization trigger that responds to irradiance regime changes; “intelligent” denotes robust regime shift detection and safe duty ratio clamping. Across uniform, step change, and partial shading conditions, the proposed AIPSO achieves fast reconvergence and high tracking efficiency with negligible steady state oscillations, as summarized in the results. Building on this contribution, future research will focus on evaluating its scalability across different PV architectures and large-scale grid integration with real hardware setup.
Volume: 16
Issue: 4
Page: 2841-2859
Publish at: 2025-12-01

Advanced control architectures for enhanced simulation and operational analysis of solar PV-driven vehicle systems

10.11591/ijpeds.v16.i4.pp2615-2622
Raghupathi Mani , Susitra Dhanraj , Karthikeyan Nagarajan
Interplanetary interest in solar PV systems in automobiles has grown as renewable energy, especially in transportation subsystems, is used more widely. Emphasizing innovative control strategies to increase power conversion efficiency, reliability, and flexibility, this paper identifies and assesses solar photovoltaic integrated vehicle drive systems. In Simulink, several researchers replicate power systems, solar PV systems, vehicle propulsion systems, and power conversion technologies. To imitate real-world settings, researchers evaluate the efficiency of the device at many solar light and load values. High-level control techniques suitable in such unpredictable conditions are MPPT and dynamic load control. These controls are definitely required to ensure the correct functioning of the plant system, independent of natural variables, like irradiation and temperature. After that, the performance of the suggested control strategies is investigated under the main success criteria: energy analysis, system efficiency, and operational stability. This implies that solar PV integrated systems for automobiles could gain from ideal performance and durability, hence improving the off-grid operation of cars. These findings offered latent promise for use in the developing transportation sector and advancement of solar PV technology.
Volume: 16
Issue: 4
Page: 2615-2622
Publish at: 2025-12-01

Small signal modeling of restructured boost converter in continuous conduction mode

10.11591/ijpeds.v16.i4.pp2500-2508
Anwar Muqorobin , Sulistyo Wijanarko , Muhammad Kasim , Pudji Irasari , Ketut Wirtayasa , Puji Widiyanto
This paper introduces small signal modeling of the restructured boost converter (RBC) in continuous conduction mode (CCM) by using the circuit averaging technique. The averaging technique produces linear transfer functions of the converter. The transfer functions relating the duty cycle to output voltage, duty cycle to inductor current, input voltage to output voltage, and input voltage to inductor current are obtained. To validate the converter model, power simulation (PSIM) simulations are developed, and experiments are conducted. The function of RBC is similar to a conventional boost converter, i.e., to level up the input voltage. A comparative analysis between the RBC and conventional boost converter is performed. The results highlight the advantages of RBC over a conventional boost converter.
Volume: 16
Issue: 4
Page: 2500-2508
Publish at: 2025-12-01

Lithium-ion battery charge-discharge cycle forecasting using LSTM neural networks

10.11591/ijpeds.v16.i4.pp2831-2840
Vimala Channapatana Srikantappa , Seshachalam Devarakonda
An important component for the dependable and safe utilization of lithium-ion batteries is the ability to accurately and efficiently predict their remaining useful life (RUL). In this research, a long short-term memory recurrent neural network (LSTM RNN) model is trained to learn from sequential data on discharge capacities across different cycles and voltages. The model is also designed to function as a cycle life predictor for battery cells that have been cycled under varying conditions. By leveraging experimental data from the NASA battery dataset, the model achieves a promising level of prediction accuracy on test sets consisting of approximately 200 samples.
Volume: 16
Issue: 4
Page: 2831-2840
Publish at: 2025-12-01

Supervised learning for fast inverse motor control mapping: a comparative study on SRM and BLDC motors

10.11591/ijpeds.v16.i4.pp2419-2428
S. Sudheer Kumar Reddy , J. N. Chandra Sekhar
This paper investigates the application of machine learning (ML) models, specifically artificial neural networks (ANN) and XGBoost, for real-time motor control, focusing on switched reluctance motors (SRM) and brushless DC motors (BLDC). Traditional inverse dynamics mapping for motor control is compared with ML approaches to highlight advantages in speed, accuracy, and deployment efficiency. Datasets simulating the input-output behavior of both motor types are used to train and test the models. Key performance metrics such as mean squared error (MSE), R² score, training time, and latency are evaluated, with the goal of replacing traditional control methods in real-time applications. Results indicate that ML models outperform traditional methods in terms of prediction accuracy and deployment speed, suggesting a promising path toward more efficient and adaptive motor control systems. The novelty of this work lies in applying supervised learning directly for inverse motor control mapping, thereby eliminating the need for explicit analytical models and enabling a unified, data-driven benchmarking framework across SRM and BLDC.
Volume: 16
Issue: 4
Page: 2419-2428
Publish at: 2025-12-01

Design and implementation of IoT-based soft starter for induction motor

10.11591/ijpeds.v16.i4.pp2170-2177
Laith Najem Abood Khudhur , Amer Abdulmahdi Jabbar Chlaihawi
The practical application of the induction motor is an essential part of electrical engineering. A direct connection of the motors to the mains voltage negatively affects both the motor itself and the mains system as a whole due to high starting current values, as a result, more accidents and shortening the drive system service life. This article discusses the development of designing and implementing of soft starter single-phase IM to reduce the inrush current using the firing angle reduction technique with remote monitoring and control using the ESP32 (node MCU) and Arduino Due microcontrollers. The integration of IoT-based tools software such as VS Code, enables the remote monitoring and control of motor features. Testing shows that the system effectively facilitates remote motor control, providing a flexible and accessible learning environment with minimum starting current, solving the inrush current problem facing IMs. The proposed soft starter gives three cases of firing angle reduction that show a percentage reduction in starting current for these cases (case I, case II, and case III) are 51%, 54% and 64%, respectively. Case III has a maximum starting current is 2.2 A compared to 6.2 A for direct connecting of IM to the power supply (DOL).
Volume: 16
Issue: 4
Page: 2170-2177
Publish at: 2025-12-01

ANN-based MPPT for photovoltaic systems: performance analysis and comparison with nonlinear and classical control techniques

10.11591/ijpeds.v16.i4.pp2780-2791
Khadija Abdouni , Mostafa Benboukous , Drighil Asmaa , Hicham Bahri , Mohamed Bour
In photovoltaic energy systems, maximum power point tracking (MPPT) techniques are essential for optimizing power output under changing climatic conditions. Several techniques have been proposed in the literature, including classical techniques such as perturb and observe (P&O) and incremental conductance (INC), nonlinear controllers such as backstepping, and artificial intelligence-based techniques like fuzzy logic. This study compares the performance of an artificial neural network (ANN)-based MPPT approach with these nonlinear and classical MPPT techniques. It analyses the advantages and limitations of the various techniques to evaluate their performance in terms of efficiency, accuracy, and output power stability under changing climatic conditions. The study aims to help researchers select the most effective technique to improve the efficiency of photovoltaic systems. The simulation was carried out using MATLAB/Simulink. The simulation results indicated that the artificial neural network achieved better performance than the other techniques in terms of tracking speed, with an efficiency of up to 99.94%, while maintaining stable output power under changing climatic conditions. The backstepping controller also showed stable output power compared to traditional techniques. Fuzzy logic had a lower efficiency than both the artificial neural network and backstepping. Perturbation and observe and incremental conductance are easy to implement, but they showed oscillations around the maximum power point, which reduces the overall efficiency of the system.
Volume: 16
Issue: 4
Page: 2780-2791
Publish at: 2025-12-01
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