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30,376 Article Results

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

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

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

Fault diagnosis for inverter open circuit faults using DC-link signal and random forest-based technique

10.11591/ijpeds.v16.i4.pp2178-2185
Hoang-Giang Vu , Dang Toan Nguyen
Three-phase voltage source inverters based on insulated-gate bipolar transistors (IGBTs) are widely used in various industrial applications. Faults in IGBTs significantly affect the performance of the inverter and entire system. Robust and accurate fault detection are the key requirements of fault diagnosis methods. This paper explores a method for diagnosing power switch open circuit faults of a voltage source inverter based on machine learning algorithms. The diagnosis is performed in two steps, firstly the fault is detected by applying the Random Forest classifier algorithm with the DC-link signal. Next, the fault switch location is performed by additionally using the inverter output AC current signals. The diagnostic results based on simulation data show that the fault can be detected with maximum accuracy. Meanwhile, the accuracy in locating the fault switch is also significantly improved with the additional use of current signals measured at the DC-link. Potential application of electromagnetic field signal is also highlighted for the practical implementation of fault diagnosis.
Volume: 16
Issue: 4
Page: 2178-2185
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

Enhanced speed regulation using separate P and I gain controllers in a fuzzy-PI framework

10.11591/ijpeds.v16.i4.pp2280-2295
Minh Duc Pham , Duong Nguyen Trong Qui , Truong Phuoc Hoa
This paper explores an enhanced method for regulating the speed of brushless DC (BLDC) motors using field-oriented control. Conventionally, a proportional-integral (PI) controller is employed to adjust output speed and current FOC method. While the PI controller is effective in many scenarios, it exhibits limitations including poor performance when the speed reference changes rapidly. To address these limitations, a fuzzy-PI control scheme is proposed in this study with the aim of improving the speed control performance of BLDC motors, especially under rapidly changing speed reference. The proposed two separate fuzzy logic controllers adaptively adjust the proportional and integral gains so that it combines the robustness of fuzzy logic with the steady-state error of PI control. Simulation and experimental results demonstrate that the fuzzy-PI control significantly outperforms the conventional PI controller in terms of BLDC stability, response time, and accuracy. The proposed approach ensures more reliable and efficient speed regulation for BLDC motors, making it a reliable solution for applications where speed reference fluctuate frequently.
Volume: 16
Issue: 4
Page: 2280-2295
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

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

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

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

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

Adaptive fuzzy logic controller based BLDC motor to improve the dynamic performance for electric tractor application

10.11591/ijpeds.v16.i4.pp2186-2196
Ashwini Yenegur , Mungamuri Sasikala
Permanent magnet brushless DC (PMBLDC) motors are widely used in a variety of industrial applications due to their high-power density and ease of regulation. The three-phase power semiconductors bridge is the standard way for controlling these motors. In order to initiate the inverter bridge and switch on the power devices, rotor position sensors must be provided with the correct commutation sequence. The power devices commutate progressively 60 degrees, depending on the location of the rotor. The right speed controllers are necessary for the motor to run as efficiently as possible. PI controllers are commonly employed with permanent magnet motors to achieve speed control in simple manner. Nevertheless, these controllers provide challenges in managing control complexity, including nonlinearity, parametric fluctuations, and load disturbances. PI controllers need accurate linear mathematical models. To overcome this, in this paper adaptive fuzzy logic controller (FLC) for controlling the speed of a BLDC motor is presented. When the motor drive system uses the adaptive FLC technology for speed control, it exhibits better dynamic behavior and is more resistant to changes in parameters and load disturbances. The main objectives of this work are to analyze and appraise the functioning of an electric tractor driven by a PMBLDC motor drive using adaptive FLC. The PMBLDC motor drive controllers are simulated using MATLAB/Simulink software.
Volume: 16
Issue: 4
Page: 2186-2196
Publish at: 2025-12-01

Improvement of DSIM control using fuzzy third-order sliding mode approach optimized by MOA

10.11591/ijpeds.v16.i4.pp2321-2331
Rahma Belkaid , Lamia Youb , Farid Naceri , Ghoulem Allah Boukhalfa
This study focuses on the contribution of a new hybrid controller based on the sliding mode technique associated with fuzzy logic and optimized by an innovative approach called the mayfly optimization algorithm (MOA) to improve the drive of the dual star induction motor (DSIM). The performance and robustness of this system are analyzed under different operating conditions with three proposed strategies and compared with each other under the MATLAB/Simulink environment. Through the simulation results obtained, we realize that the method that integrates the MOA with a hybrid controller associating the third order sliding mode with fuzzy logic (MOA-FTOSMC) makes a significant contribution to research work in this field and offers the best dynamic performance and adequately manages the uncertainty and variation of the system parameters under different operating regimes.
Volume: 16
Issue: 4
Page: 2321-2331
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

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
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