cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Power Electronics and Drive Systems (IJPEDS)
Published by Institute of Advanced Engineering and Science
ISSN : -     EISSN : 20888694     DOI : -
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Power Electronics and Drive Systems (IJPEDS, ISSN: 2088-8694, a SCOPUS indexed Journal) is the official publication of the Institute of Advanced Engineering and Science (IAES). The scope of the journal includes all issues in the field of Power Electronics and drive systems. Included are techniques for advanced power semiconductor devices, control in power electronics, low and high power converters (inverters, converters, controlled and uncontrolled rectifiers), Control algorithms and techniques applied to power electronics, electromagnetic and thermal performance of electronic power converters and inverters, power quality and utility applications, renewable energy, electric machines, modelling, simulation, analysis, design and implementations of the application of power circuit components (power semiconductors, inductors, high frequency transformers, capacitors), EMI/EMC considerations, power devices and components, sensors, integration and packaging, induction motor drives, synchronous motor drives, permanent magnet motor drives, switched reluctance motor and synchronous reluctance motor drives, ASDs (adjustable speed drives), multi-phase machines and converters, applications in motor drives, electric vehicles, wind energy systems, solar, battery chargers, UPS and hybrid systems and other applications.
Arjuna Subject : -
Articles 2,594 Documents
 256   257   258   259   260 
Bidirectional AC/DC converter connecting AC and DC microgrids for smart grids Dung, Nguyen Van; Vinh, Nguyen The
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2549-2561

Abstract

This paper proposes a converter connecting two independent AC and DC microgrids in a flexible microgrid and smart grid system. With this converter, basic DC/DC converter types such as Flyback are used to develop the power circuit and controller for the converter that is capable of integrating the operating functions for the operation between microgrids. The converter uses bidirectional switching locking technology to simplify the control algorithm. The energy is converted in two directions, AC/DC and DC/AC, with different working principles of increasing and decreasing voltage according to the standards of the distribution grid and DC microgrid. The TDH value is significantly limited when using the recovery circuit solution. The converter is designed, simulated based on OrCAD software, and tested with a capacity in the range of 2-10 kW. The DC microgrid output voltage is 400 VDC, voltage is 220 VAC.
Enhanced integration of renewable energy and smart grid efficiency with data-driven solar forecasting employing PCA and machine learning Kathirvel, Jayashree; Sreenivasan, Pushpa; Vanitha, M.; Mohammed, Soni; Kumar, T. Sathish; Adaikalam, I. Arul Doss
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2645-2654

Abstract

A significant obstacle to preserving grid stability and incorporating renewable energy into smart grids is variations in solar irradiation. To improve solar power management's dependability, this research proposes a short-term solar forecasting framework powered by AI. Multiple machine learning models, such as long short-term memory (LSTM), random forest (RF), gradient boosting (GB), AdaBoost, neural networks (NN), K-Nearest neighbor (KNN), and linear regression (LR), are integrated into the suggested system, which also uses principal component analysis (PCA) for dimensionality reduction. The Abiod Sid Cheikh station in Algeria (2019-2021) provided real-world data for the model's validation. With a two-hour-ahead RMSE of 0.557 kW/m², AdaBoost had the most accuracy, whereas LR had the lowest, at 0.510 kW/m². In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. These findings highlight the efficiency of hybrid AI models based on PCA for accurate forecasting, which is crucial for smart grid stability.
Design of a static synchronous compensator for the north-south high-speed railway system Anh, An Thi Hoai Thu; Cuong, Tran Hung
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2369-2380

Abstract

The modern high-speed rail system plays a crucial role in driving the nation’s economic development. The problem of voltage imbalance caused by intermittent load movements is a significant challenge for energy management and distribution. When electric trains are connected to the three-phase grid, power quality degradation occurs, resulting in distortion and imbalance of the three-phase grid current and voltage, which in turn increases operating costs. This paper has proposed a linear control method using a PI controller for a static synchronous compensator (STATCOM) to directly control the amount of reactive power loss for electric trains. This solution will also bring good and stable voltage quality to electric trains so that electric trains can operate for a long time. The STATCOM device in this paper is a three-phase voltage source converter with a simple structure and can be easily controlled. This is considered a simple and effective solution to balance voltage, improve power factor, and enhance harmonic quality for railway trains, thereby achieving an optimal operating solution. This discussion can be simulated using MATLAB/Simulink software to determine the operation and control steps for STATCOM, thereby improving the quality of the power system. The simulation results of current, voltage, and reactive power response are presented. The simulation results have demonstrated that the proposed algorithm successfully achieves the set goals of ensuring voltage stability and providing the necessary amount of reactive power for the train, thereby improving the quality of the power grid for the North-South high-speed train in Vietnam.
Advanced thermal modeling of lithium-ion batteries: foundations for advanced capacity prediction Elkake, Abdelhadi; Laadissi, El Mehdi; Mossaddek, Meriem; Abdelhakim, Tabine; Hajajji, Abdelowahed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2699-2710

Abstract

Thermal modeling of lithium-ion batteries is crucial for optimizing their performance and reliability in applications such as electric vehicles and energy storage systems. This study introduces a novel thermal modeling framework to predict internal battery temperature as a function of current and ambient temperature. Three advanced methodologies, NN-LM, NN-BR, and GPM, were evaluated using drive cycle data across temperatures that vary from -20 °C to 25 °C. Among these, Gaussian process modeling (GPM) demonstrated the highest accuracy with an RMSE of 0.034%, while NN LM achieved an RMSE of 0.083%, offering a computationally efficient alternative suitable for real-time applications. The developed thermal model establishes a foundation for future research aimed at predicting battery capacity by incorporating the effects of internal temperature. Furthermore, accurate monitoring of internal temperature is critical for preventing thermal runaway by enabling early detection of unsafe thermal conditions. This work establishes a robust foundation for future research, aiming to develop real-time capacity prediction models, ultimately enhancing battery management systems under diverse operating conditions.

Page 260 of 260 | Total Record : 2594

 256   257   258   259   260 

Filter by Year

2011 2025


Reset
Filter By Issues
All Issue Vol 16, No 4: December 2025 Vol 16, No 3: September 2025 Vol 16, No 2: June 2025 Vol 16, No 1: March 2025 Vol 15, No 4: December 2024 Vol 15, No 3: September 2024 Vol 15, No 2: June 2024 Vol 15, No 1: March 2024 Vol 14, No 4: December 2023 Vol 14, No 3: September 2023 Vol 14, No 2: June 2023 Vol 14, No 1: March 2023 Vol 13, No 4: December 2022 Vol 13, No 3: September 2022 Vol 13, No 2: June 2022 Vol 13, No 1: March 2022 Vol 12, No 4: December 2021 Vol 12, No 3: September 2021 Vol 12, No 2: June 2021 Vol 12, No 1: March 2021 Vol 11, No 4: December 2020 Vol 11, No 3: September 2020 Vol 11, No 2: June 2020 Vol 11, No 1: March 2020 Vol 10, No 4: December 2019 Vol 10, No 3: September 2019 Vol 10, No 2: June 2019 Vol 10, No 1: March 2019 Vol 9, No 4: December 2018 Vol 9, No 3: September 2018 Vol 9, No 2: June 2018 Vol 9, No 1: March 2018 Vol 8, No 4: December 2017 Vol 8, No 3: September 2017 Vol 8, No 2: June 2017 Vol 8, No 1: March 2017 Vol 7, No 4: December 2016 Vol 7, No 3: September 2016 Vol 7, No 2: June 2016 Vol 7, No 1: March 2016 Vol 6, No 4: December 2015 Vol 6, No 3: September 2015 Vol 6, No 2: June 2015 Vol 6, No 1: March 2015 Vol 5, No 4: 2015 Vol 5, No 3: 2015 Vol 5, No 2: 2014 Vol 5, No 1: 2014 Vol 4, No 4: December 2014 Vol 4, No 3: September 2014 Vol 4, No 2: June 2014 Vol 4, No 1: March 2014 Vol 3, No 4: December 2013 Vol 3, No 3: September 2013 Vol 3, No 2: June 2013 Vol 3, No 1: March 2013 Vol 2, No 4: December 2012 Vol 2, No 3: September 2012 Vol 2, No 2: June 2012 Vol 2, No 1: March 2012 Vol 1, No 2: December 2011 Vol 1, No 1: September 2011 More Issue