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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 2,660 Documents
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Controlling parameters proportional integral derivative of DC motor using a gradient-based optimizer Aribowo, Widi; Rahmadian, Reza; Widyartono, Mahendra; Wardani, Ayusta Lukita; Prapanca, Aditya; Abualigah, Laith
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i2.pp696-703

Abstract

In this paper, a gradient-based optimizer (GBO) algorithm is presented to optimize the parameters of a proportional integral derivative (PID) controller in DC motor control. The GBO algorithm which mathematically models and mimics is inspired by the gradient-based Newton method. It was developed to address various optimization issues. To determine the performance of the proposed method, a comparison method with the ant colony optimization (ACO) method. It was compared using the integral of time multiplied absolute error (ITAE). They are most popularly used in the literature. From the test results, the proposed method is promising and has better effectiveness. The proposed method, namely GBO-PID, shows the best performance.
Enactment of HBMMC five-level inverter based D-STATCOM using robust controllers Ramakrishna, Elluru; Jayakrishna, Gadhamappagari; Peddakotla, Sujatha
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i2.pp1001-1008

Abstract

This paper provides the results of a concerted investigation into developing a robust voltage controller for the modular multilevel converter (MMC) used in the D-STATCOM. The simplicity of the half bridge (HB) sub-module, along with the fact that it has minimal conduction losses, led to its adoption in the MMC. For the purpose of controlling the HB MMC switches, an enhanced modulation strategy based on the phase disposition (PD) scheme has been implemented. In the presence of nonlinear load conditions, the converters are experiencing difficulties with the DC voltage balancing. The sliding mode controller (SMC) is utilized so that the DC voltage can be maintained in a balanced state. Performance characteristics including active power, reactive power, dc voltage control and THD are shown and compared. For the purpose of simulating the indicated methodology, MATLAB/Simulink is the tool of choice.
Designing an optimal PID controller for a PV-connected Zeta converter using genetic algorithm Hussain, Abadal-Salam T.; Taha, Faris Hassan; Fadhil, Hilal A.; Salih, Sinan Q.; Taha, Taha A.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i1.pp566-576

Abstract

This paper suggests a way of fixing problems of voltage fluctuations and peak overshoot in a PV-connected Zeta converter system. The Zeta converter in the proposed approach is controlled using proportional integral derivative (PID) while a genetic algorithm (GA) calculates the PID coefficients based on the control mechanism. The performance of the designed system was analyzed in a MATLAB/Simulink environment. The analysis showed that the proposed system reduced the output voltage ripple and peak overshoot during transient conditions by providing feedback to the converter through the PID controller, this is a significant improvement when compared to the results found without a PID controller.
Machine learning-based lithium-ion battery life prediction for electric vehicle applications Ha, Vo Thanh; Vinh, Vo Quang; Truc, Le Ngoc
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.pp1934-1941

Abstract

The actual and anticipated battlefield creates a model capable of accurately estimating the lifetime of lithium-ion batteries used in electric cars. This inquiry uses a technique known as supervised machine learning, more particularly linear regression. In lithium-ion batteries, modeling temperature-dependent per-cells is the basis for capacity calculation. When a sufficient quantity of test data is accessible, a linear regression learning method will be utilized to train this model, ensuring a positive outcome in forecasting battery capacity. The conclusions drawn in the article are derived from the attributes of the initial one hundred charging and discharging cycles of the battery, enabling the determination of its remaining power. This determination facilitates the swift identification of battery manufacturing procedures and empowers consumers to detect flawed batteries when signs of performance degradation and reduced longevity are observed. MATLAB simulations have demonstrated the accuracy of the projected results, exhibiting a margin of error of approximately 9.98%. With its capacity to provide a reliable and precise means of estimating battery lifespan, the developed model holds the potential to revolutionize the electric vehicle industry.
Modelling and performance analysis of free body dynamics of electric vehicles Sakthivelsamy, Rajalingam; Subramaniyan, Kanagamalliga
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i1.pp1-7

Abstract

The modelling and performance analysis of free body dynamics in electric vehicles (EVs) plays a crucial role in understanding and optimizing the vehicle's behavior and performance. This research focuses on accurately modelling the forces and motions acting on the vehicle body, excluding the powertrain components. By considering factors such as vehicle weight, suspension characteristics, tire properties, and aerodynamic forces, a comprehensive mathematical model is developed. This model enables the simulation and analysis of the vehicle's behavior under various operating conditions. The performance analysis involves evaluating key metrics such as vehicle response, stability limits, and ride comfort. The proposed system is compared with the existing electric vehicle in the market. The findings from this research contribute to the design and development of EVs with improved handling, stability, and energy efficiency. Additionally, they inform the development of advanced driver-assistance systems (ADAS) and autonomous driving technologies. Overall, the modelling and performance analysis of free body dynamics in EVs supports the advancement of sustainable and efficient transportation systems.
Dual-axis solar tracker system utilizing Fresnel lens for web-based monitoring Ayu, Humairoh Ratu; Kurniawansyah, Rifki Mohamad; Supriyanto, Amir; Junaidi, Junaidi
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.pp1799-1809

Abstract

Solar energy produced using solar panels is a renewable source of electricity. Over the years, several studies have been developed in the field to increase the performance efficiency of these panels. Therefore, this study aims to develop dual-axis solar tracker with the addition of Fresnel lens to improve performance efficiency. The system implemented consisted of multisensors, servo motors, Fresnel lenses, Arduino nano, and NodeMCU ESP32. In the experiments, proposed tracking system with and without Fresnel lens were evaluated to compare the output of both setups. The results showed that the maximum power of dual-axis solar tracker with and without the device was 13.60 W and 15.78 W, respectively, at the same radiation intensity, temperature, and time. These findings showed that the proposed tracking system could increase the maximum power efficiency of solar panels by 16.03%. Furthermore, the maximum value was obtained when dual-axis solar tracker with Fresnel lens moved from E to W at 23° to the horizontal.
Performance improvement of a standalone PV system using supercapacitors: modeling and energy management Hassan, Mohamed Salah; Hassan, Shimaa; Hassan, Mohamed Reda Mahmoud; El-Sayed, Abou-Hashema Mostafa; Shoyama, Masahito; Dousoky, Gamal Mahmoud
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i1.pp222-238

Abstract

Standalone photovoltaic (PV) systems are the most common and practical application in remote areas and communities far from the power grid. However, in the case of supplying a pulsating load with only a battery as a storage unit, their performance degraded. Therefore, hybrid electrical energy storage (HEES) systems represent a viable solution. This paper investigates the impact of utilizing a supercapacitor (SC) to work cooperatively with a battery storage unit to enhance the overall system behavior. Two scenarios of battery storage systems with/without SC are considered. A comprehensive modeling and sizing approach is established and presented in detail. Then, an energy management system (EMS) is proposed to enhance the HEES system’s performance. A proportional-integral (PI)-based controller is designed and examined to control the power electronic converters and hence improve energy management. The HEES system operation is simulated and evaluated using MATLAB/Simulink to feed a pulsating load, where the drawn pulsated load current is composed of two components: one component is supplied by battery, and the other component is fed from SC. Finally, the performance of the two hybrid configurations is evaluated in terms of battery voltage and current fluctuations, transient response, and load voltage and current ripples. The obtained results demonstrate the effectiveness of introducing SCs into HEES system.
Revolutionizing motor maintenance: a comprehensive survey of state-of-the-art fault detection in three-phase induction motors Bahgat, Bahgat Hafez; Elhay, Enas A.; Sutikno, Tole; Elkholy, Mahmoud M.
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.pp1968-1989

Abstract

This comprehensive review delves into electrical machine fault diagnosis techniques, with a particular emphasis on three-phase induction motors. It covers a variety of faults, including eccentricity, broken rotor bars, and bearing faults. It also covers techniques like motor current signature analysis (MCSA), partial discharge testing, and artificial intelligence (AI)-based approaches. This review focuses on fault diagnosis techniques for electrical machines, specifically eccentricity faults, squirrel cage rotor faults, and bearing faults. It discusses their efficacy, applications, and limitations, as well as the role of AI and neural network techniques in modern fault detection applications. The review covers not only eccentricity faults, but also stator or armature faults caused by insulation failure, as well as bearing faults classified as ball, train, outer, and inner races. It focuses on early detection to ensure optimal machine performance and reliability, while also providing insights into fault detection mechanisms. Modern ways of finding problems with machines, like non-negative matrix factorization, rectified stator current analysis, incremental broad learning, and AI-based methods, make machines work better and stop money from being lost. The review is a valuable resource for practitioners and researchers in the field, allowing them to make better decisions about maintenance strategies and increase machine efficiency.
Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction Ponkumar, Ganesapandiyan; Jayaprakash, Subramanian; Ramasamy, Dharmaprakash; Priyasivakumar, Amudha
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i2.pp1211-1217

Abstract

In our proposed approach, we integrate ADA boosting with particle swarm optimization-extreme learning machine (PSO-ELM) to enhance the accuracy of wind power estimation, addressing the inherent unpredictability and variability in wind energy. Initially, we refine the thresholds and input weights of the extreme learning machine (ELM) and then construct the PSO-ELM prediction model. ADA Boost is utilized to generate multiple weak predictors, each comprising a distinct hidden layer node. The PSO technique is then employed to optimize the input weights and thresholds for each weak predictor. The final forecast is attained by amalgamating and weighting the outcomes from each weak predictor using a robust wind power forecast model. Experimental validation utilizing data from Turkish wind turbines underscores the efficacy of our approach. Comparative analysis against contemporary techniques such as ensemble learning models and optimal neural networks reveals that our ADA-PSO-ELM model demonstrates superior accuracy and generalizability in predicting wind power output under real-world conditions. The proposed approach offers a promising framework for addressing the challenges associated with wind power estimation, thereby facilitating more reliable and efficient utilization of wind energy resources.
Validation and test of a novel multi-input converter in extreme conditions using PSOPIC in hybrid power generation environment Jayaprakash, Supriya; Siddamallaiah, Rajashekar Jangam
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i2.pp1247-1253

Abstract

The need for renewable energy resources increases due to the day-by-day increase in load demand. Still, there is a need for new technology to operate the existing power system optimally. Distributed generation (DGENs) are helpful in meeting the demand of power due to its lower cost compared to the construction of the complete power system. But this DGEN is constructed using renewable resources, which are intermittent in nature. So, hybrid power generation, which uses multiple sources, is used to satisfy the power need. In this paper, validation, and test of a new multi-input single ended primary inductor converter (MI-SEPIC) is proposed. The performance of the MI-SEPIC converter is tested by connecting photovoltaic (PV), wind, and fuel cells. The proposed system is connected to the grid, and the power transients are analyzed. The DQ control of grid synchronization is discussed in this paper. The conventional PI controller is replaced with a hybrid particle swarm optimization tuned proportional integral control (PSOPIC), and the results are compared. Verification is done using MATLAB software. Validation and test with different test cases to prove the sturdiness of the complete system is explained.

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