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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 65 Documents
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Search results for , issue "Vol 15, No 3: September 2024" : 65 Documents clear
Management and monitoring of lithium-ion battery recharge with ESP32 Gomez-Huaylla, Estefany; Mejía-Cruz, Luis; Paiva-Peredo, Ernesto
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1677-1686

Abstract

Air quality is important for human health, the use of clean energy is one way to improve it. And the management and monitoring of the recharge of ion-lithium batteries used in electric vehicles and other devices requires efficient systems. The objective is to develop an intelligent electrical recharging system for lithium-ion batteries using internet of thing (IoT) technology. In this article, an electrical recharging system for lithium-ion batteries was designed and carried out, which is made up of a source, a diode bridge, L298 n driver, current sensor, a voltage divider sensor and the ESP32 microcontroller. The system determines the storage capacity of the battery and monitor it remotely via WIFI. The data is sent to a Shiftr.io server and graphically displayed on a NODE RED platform. The message queuing telemetry transport (MQTT) protocol is used to communicate the devices and decide the best time to recharge the batteries. The results show that the system works correctly and offer useful information that optimizes the charging process, it contributes to improving savings in the payment of electricity consumption and the use of clean energy. The limitations of the study are the small sample size and the lack of comparison with other similar systems.
African vulture optimizer algorithm for fuzzy logic speed controller of fuel cell electric vehicle Elnaghi, Basem E.; Dessouki, Mohamed Elshahat; Mohamed, Sara Wahied; Ismaiel, Ahmed M.; Abdel-Wahab, Mohamed Nabil
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1348-1357

Abstract

This research article introduces a novel optimization strategy for fuel cell electric vehicles (FCEVs) in order to reduce the integral square error to enhance dynamic performance. African vulture optimizer algorithm (AVOA) improves a speed fuzzy logic controller's (FLC) internal controller settings. The AVOA is renowned for its simplicity in implementation, and low demand on computational resources. The speed drive of FCEV is investigated using MATLAB/Simulink 2023a. The results of FLC-AVOA provide lower settling time, lower overshoot, lower undershoot, and high dynamic response when compared to FLC and proportional-integral (PI) controllers designed using genetic algorithm (GA). The FLC-AVOA reduced the rising time for speed dynamic response by 2.31% and the maximum peak overshoot by 55.23% as compared to FLC-GA.
Machine learning applications for predicting system production in renewable energy Somasundaram, Deepa; Muthukumar, R.; Rajavinu, N.; Ramaiyan, Kalaivani; Kavitha, P.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1925-1933

Abstract

Renewable energy systems play pivotal role in addressing the global challenge of sustainable energy production. Efficiently harnessing energy from renewable sources requires accurate prediction models to optimize system production. This paper delves into the realm of predictive modeling, focusing on the utilization of machine learning techniques to forecast system production in renewable energy systems. The investigation incorporates a range of factors such as wind speed, sunshine, air pressure, radiation, air temperature, and relative air humidity, alongside temporal data ('Date-Hour (NMT)'). These factors undergo rigorous curation and preprocessing to ensure the reliability and quality of the predictive model. Various machine learning algorithms, including linear regression, decision tree, random forest, and support vector machine (SVM), are employed to examine the relationships between these factors and system production. The findings are assessed using metrics such as mean squared error, mean absolute error, and R-squared. Through comparative analysis, the study illuminates the strengths and limitations of each algorithm, providing valuable insights into their suitability for renewable energy forecasting. This paper adds to renewable energy research by examining how machine learning predicts system production. The insights are valuable for researchers, practitioners, and policymakers in sustainable energy development.
Enhancing stability and voltage quality in remote DC microgrid systems through adaptive droop control approach Lam, Hong Phuc; Nguyen, Hung Duc; Pham, Minh Duc
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1456-1467

Abstract

To ensure the stable and accurate operation of "rural areas”, a reliable power source is necessary, and voltage issues must be carefully considered in power system design to ensure patient safety. Remote DC microgrids provide a viable option for transferring energy across power sources while assuring stability and high efficiency. In this paper, an adaptive droop control approach is developed and compared to the standard droop control method. The suggested technique recommends a dynamic modification of droop coefficients intending to effectively limit the buildup of mistakes in current sharing and departures from the preset voltage setpoints. Through the implementation of the adaptive droop control method, the remote DC microgrid not only enhances current balancing performance but also contributes to a substantial improvement in voltage stability, thereby increasing the overall operational efficiency of the system. Simulation and experimental results on a small-scale remote DC microgrid validate the proposed adaptive droop control approach, proving its effectiveness in the small-scale microgrid system.
Artificial rabbits optimization based reconfiguration and distributed generation allotment in the distribution network Rao, Ganney Poorna Chandra; Babu, Pallikonda Ravi; Rupesh, Mailugundla; Krishna, Puvvula Venkata Rama
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1749-1756

Abstract

For the past few years, to reduce system power losses and maintain operating constraints, such as voltage stability, network reconfiguration has been crucial in determining the radial operating framework. Distributed generation (DG) is typically used to generate energy close to the site of consumption. This technology generates energy that is affordable, in contrast to conventional energy production. To lessen energy losses as well as boost voltage characteristics, the adopted methodology is centered on reconfiguration and DG distribution in the radial distribution network (RDN). In this work, the loss sensitivity factor (LSF) is used to determine the right DG position in RDN. After identifying the bus for DG positioning, the artificial rabbits optimization (ARO) technique is used to ascertain the ideal reconfigured network and DG size to lessen energy losses and enhance the voltage profile for RDN. The employed methodology is investigated on IEEE-33 and 69 RDN, respectively, for two cases of considering only reconfiguration without distributing units of DG and reconfiguration with an allotment of three DG units. The latter case showed better results compared to the case of only reconfiguration.

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