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31,456 Article Results

Nonlinear excitation control of multimachine systems via the invariant-set design

10.11591/ijpeds.v16.i4.pp2332-2341
Hisham M. Soliman , Ehab H. E. Bayoumi , Farag Ali El-Sheikhi , Fawzan Salem
Power grids are inherently vulnerable to many uncertainties. All power networks are prone to instability because of the uncertainties inherent in the operation of power systems. Rotor-angle instability is a challenging issue, and if not properly managed, could give rise to cascading failures and even blackouts. This paper addresses the generator excitation system’s state feedback sliding mode control (SMC). The global system is divided into multiple subsystems to achieve decentralized control. A disturbance is defined as the influence of the system as a whole on a specific subsystem. The state-feedback controller is to be designed taking into account the disturbance attenuation level, ensuring the closed-loop system's asymptotic stability. The SMC designing algorithm is described; it is based on precisely determining the sliding surface utilizing the invariant-set (ellipsoid) technique. The control structure ensures that mismatched disturbances in power systems have little impact on the system trajectory in the sliding mode. Moreover, the proposed controllers are represented in this paper using linear matrix inequalities (LMIs) and the Lyapunov theory approach. Finally, a multi-machine model is implemented to demonstrate the success of the suggested approach, and a comparison between the proposed SMC and the conventional one demonstrates its superiority.
Volume: 16
Issue: 4
Page: 2332-2341
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

Optimized passive and active shielding of magnetic induction generated by ultra-high-voltage overhead power lines

10.11591/ijece.v15i6.pp5144-5161
Salah-Eddine Houicher , Rabah Djekidel , Sid Ahmed Bessidek
This paper presents computational modeling to assess and limit the magnetic induction levels emitted by an extra-high-voltage (EHV) overhead transmission line of 750 kV using the fundamental principle of Biot-Savart law in magnetostatics. An optimization technique based on the grey wolf optimizer (GWO) algorithm is employed to determine the appropriate location of the passive and active loop conductors, and the associated parameters to shielding to achieve better compensation of magnetic induction in an interest zone. The resulting magnetic induction of the ultra high voltage (UHV) overhead power line exhibits a crest value of 27.78 μT at the middle of the right-of-way, which can be considered unacceptable by strict protection standards. Generally, the magnetic compensation loops optimally located under the phase conductors of the power transmission system reduce the magnetic induction levels along the transmission line corridor. The passive loop attenuates the maximum magnetic induction by a rate of 29.7%. Therefore, the performance of the active loop is better; it provides a greater reduction with a rate reaching 53.24%. The simulation results were tested with those derived by the elliptical polarization process. An excellent concordance was found, which made it possible to ensure the adopted method.
Volume: 15
Issue: 6
Page: 5144-5161
Publish at: 2025-12-01

Processor-in-the-loop performance validation of a three-phase NPC three-level inverter using a novel sinusoidal PWM technique for scalar control of an induction motor

10.11591/ijpeds.v16.i4.pp2257-2270
Badr N’hili , Souhail Barakat , Abdelouahed Mesbahi , Mohamed Khafallah , Ayoub Nouaiti
This paper presents the performance of a three-phase, three-level neutral point clamped inverter driving an induction motor for variable-speed applications, compared to a two-level inverter. The studied inverter operates using a novel sinusoidal pulse width modulation technique that improves the quality of voltage and current output signals while increasing efficiency. Motor speed control is achieved using the scalar control (V/Hz) method. Experimental validation of the simulation results is performed by executing the generated C code on the F28379D DSP LaunchPad within the MATLAB/Simulink and Code Composer Studio environment, applying the processor-in-the-loop (PIL) technique.
Volume: 16
Issue: 4
Page: 2257-2270
Publish at: 2025-12-01

A hybrid DMO-CNN-LSTM framework for feature selection and diabetes prediction: a deep learning perspective

10.11591/ijece.v15i6.pp5555-5569
Mutasem K. Alsmadi , Ghaith M. Jaradat , Tariq Alsallak , Malek Alzaqebah , Sana Jawarneh , Hayat Alfagham , Jehad Alqurni , Usama A. Badawi , Latifa Abdullah Almusfar
The early and accurate prediction of diabetes mellitus remains a significant challenge in clinical decision-making due to the high dimensionality, noise, and heterogeneity of medical data. This study proposes a novel hybrid classification framework that integrates the dwarf mongoose optimization (DMO) algorithm for feature selection with a convolutional neural network–long short-term memory (CNN-LSTM) deep learning architecture for predictive modeling. The DMO algorithm is employed to intelligently select the most informative subset of features from a large-scale diabetes dataset collected from 130 U.S. hospitals over a 10-year period. These optimized features are then processed by the CNN-LSTM model, which combines spatial pattern recognition and temporal sequence learning to enhance predictive accuracy. Extensive experiments were conducted and compared against traditional machine learning models (logistic regression, random forest, XGBoost), baseline deep learning models (MLP, standalone CNN, standalone LSTM), and state-of-the-art hybrid classifiers. The proposed DMO-CNN-LSTM model achieved the highest classification performance with an accuracy of 96.1%, F1-score of 94.6%, and ROC-AUC of 0.96, significantly outperforming other models. Additional analyses, including confusion matrix, ROC curves, training convergence plots, and statistical evaluations confirm the robustness and generalizability of the approach. These findings suggest that the DMO-CNN-LSTM framework offers a powerful and interpretable tool for intelligent diabetes prediction, with strong potential for integration into real-world clinical decision-support systems.
Volume: 15
Issue: 6
Page: 5555-5569
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

ANN based speed control of switched reluctance motor using MATLAB-interfaced DSP controller

10.11591/ijpeds.v16.i4.pp2243-2256
Veena Wilson , Latha Padinjaredath Govindan
The switched reluctance motor (SRM) is gaining significance as a competitive motor in industries due to its prominent features such as absence of rare-earth elements, strong fault tolerance, and competitive efficiency. This paper presents a comprehensive framework to a novel and simplified hardware implementation of SRM drive, accompanied by a stepwise procedure to develop the control process that includes system modelling with simulation analysis and experimental validation, useful for the novice researchers. A precise hardware control environment is introduced, by integrating MATLAB/Simulink platform with digital signal processor (DSP) microcontroller - TMS320F280049C, which minimizes the complexities of traditional controller coding. The paper provides an in-depth explanation of deployment of artificial neural network (ANN) speed control block, offering valuable insights into the practical aspects of ANN-based control in MATLAB. The paper also compares closed-loop speed control using proportional-integral (PI) and ANN control in SRM, and the results demonstrate accurate and adaptive performance of ANN control for variable speed- load conditions.
Volume: 16
Issue: 4
Page: 2243-2256
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

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

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

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

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

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

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
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