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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 61 Documents
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Search results for , issue "Vol 14, No 3: September 2023" : 61 Documents clear
Hybrid multi power sources PEMFC/battery/supercapacitor real time setup energy conversion system Riad Moualek; Nacereddine Benamrouche; Nabil Benyahia; Amar Bousbaine
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1844-1854

Abstract

This paper presents an energy management system with multiple sources. The proton exchange membrane fuel cell as the main power drive source is used when needed. However, because of its slow rate it is combined with a battery and supercapacitor racks as secondary sources. The proposed energy conversion system ensures the energy demand of an electric vehicle. The storage system, which includes a battery and a supercapacitor, provides a high level of performance in both autonomy and availability of power. The battery as a primary storage source, feeds the whole system during cruse processes. At needs and during accelerations phases, the supercapacitor, takes over and reacts to supply the load. This topology keeps the fuel cell disconnected from the bus supply, until the battery reaches a critical voltage level. At this time, the fuel cell kicks in and charges the battery rack. The entire system was initially tested using MATLAB/Simulink environment and the outcome findings were subsequently analyzed. The simulated results have been corroborated experimentally using a test rig based on dSPACE real time interface. Experimental results indicate that the suggested approach is capable of meeting the electric vehicle energy requirements.
Automation of the air conditioning system for aseptic rooms of pharmaceutical production José Ricardo Nuñez Alvarez; Raidel Fidel Linares Vicente; Yelena Pérez Zamora; Eliana Noriega Angarita; Rodrigo Isacc Fonnegra Rodríguez; José Javier Lozano Mendoza
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1479-1488

Abstract

As modern control systems are becoming more complex, and the implemented technology more sophisticated, the importance of the different characteristics of those systems, such as reliability, availability, security, and protection, is increasing. In this article, an automation design for the air conditioning system of classified areas of the Parenteral solutions plant of the eastern pharmaceutical laboratory company of Santiago de Cuba province, Cuba, is proposed because there is a lack of optimal environmental conditions for development and production of pharmaceuticals at a large scale. The proposal is focused on the design of a supervision system and automatic control of the environmental variables that influence the drug production process. The entire algorithm for the design was developed to monitor critical and non-critical variables and control values of temperature, relative humidity, differential pressure, and air velocity according to standards established by pharmaceutical companies. The design of the automation system includes, apart from the control and supervision algorithm for operation efficiency, the necessary instrumentation.
Application of machine learning controller in matrix converter based on model predictive control algorithm Yasoda Kailasa Gounder; Sowkarthika Subramanian
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1489-1496

Abstract

Finite control set model predictive control (FCS-MPC) algorithms are famous in power converter for its easy implementation of constraints with cost function than classical control algortihms. However computation complexity increases when swicthing state is high for converters such as matrix converter, multilevel converters and this impose a serious drawback to compute multi-step prediction horizon MPC algorithm which further increases the computation. To overcome the above said difficulty, machine learning based artificial neural network (ANN) controller for matrix converter is proposed. The training data for ANN controller is derived from MPC algorithm and trained offline with an accuracy of 70.3%. The proposed ANN controller shows a similar and better performance than MPC controller in terms of total harmonic distortion (THD), peak overshoot during dynamic change in reference current and dynamic change in load parameter and less computation with less execution time. Further, ANN controller for matrix converter is tested in OPAL-RT using hardware in-loop (HIL) simulation and showed that it outperforms MPC controller.
Short term load forecasting using evolutionary algorithm for Tajikistan Balasim M. Hussein; Hatim Ghadhban Abood; Mahmadjonov Firuz; Ivan Ivanovich Nadtoka
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1894-1900

Abstract

Load forecasting is a significant element in the energy management system of power systems. Precise load forecasting aids electric utilities to conduct decisions of unit commitment, reduction of spinning reserve capacity, and schedule device maintenance plan. Furthermore, load forecasting contributes to reducing the generation cost, and it is fundamental to the reliability of the power systems. On the other hand, short-term load forecasting is substantial for economic running. The forecasting precision directly affects the reliability, economy running and supplying power quality of the power system. Hence, finding the required load forecasting method to enhance the accuracy is valuable for forecasting precision. This paper proposed particle swarm optimization (PSO) to improve working support vector machine (SVM), SVM regression model is derived; also derived SVM with PSO. Support vector machine (SVM) model is adopted with and without PSO based on the historical load data and meteorological data of Tajikistan country, analysis the various factors affecting the forecast. The historical data and the load forecasting factors to be considered are normalized. The two parameters of SVM significantly influenced the model, and therefore it optimized using evolutionary algorithm.
Performance investigation of bridge-less power factor correction circuit with MOSFET Khudur, Khaleel Ali; Samad, Nabeel Mohamed Akram; Hasan, Ghanim Thiab
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1368-1373

Abstract

The aim of this paper is to study and investigate the performance of power factor correction (PFC) circuit implemented with the semi bridge-less configuration. The transistor-diode module APT50N60JCCU2 has been used in the proposed circuit and the UCC28070 controller has been used as a controlling device for the power factor correction (PFC) circuit. Testing has been performed in two steps. In the first step, the test was conducted on (230 Vac), while in the second step, the test was conducted on (115 Vac) as alternating input voltages. The testing results of both voltages were compared and analyzed in terms of efficiency, power factor, total harmonic distortion (THD) in order to determine the efficiency of the power factor correction circuit. The results obtained indicate that this circuit has efficiency up to 97% and a power factor close to 0.91 with the input voltage of 230 V.
Optimal design of BiCMOS second generation current conveyor using the genetic algorithm El Beqal Asmae; Benhala Bachir; Zorkani Izeddine
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1624-1632

Abstract

This article presents an innovative implementation of the plus-type second-generation current conveyor (CCII+) using BiCMOS technology, which combines the benefits of bipolar and CMOS technologies. The optimization problem of minimizing the X-port input resistance and maximizing the current cut-off frequency value was solved using genetic algorithm (GA). The proposed approach allows the optimization of the BiCMOS CCII+ to achieve better performance compared to previous designs. As a practical application, a second-order band-pass filter using the optimized BiCMOS CCII+ was successfully realized. The performance of the proposed design was evaluated using SPICE simulations and compared with previously published works. This study shows that the BiCMOS CCII+ can be effectively optimized using GA to improve its performance, which has potential applications in various analog and mixed-signal circuits.
Sliding mode controller with fuzzy supervisor for MPPT of photovoltaic pumping system Taleb Hamdi; Khaled Elleuch; Hafedh Abid; Ahmed Toumi
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1639-1650

Abstract

This paper focuses on a photovoltaic system for pumping water. The control strategy for this water pumping system is based on Takagi-Sugeno type fuzzy supervisors and sliding mode controller. The first generates the maximum power point current under varying climatic condition whereas the second allows tracking the reference signal produced by the fuzzy supervisor. The system includes a photovoltaic generator (PVG) followed by a DC-DC Converter, DC bus, an AC/DC inverter which is connected to the induction motor. This latter is coupled with a centrifuge pump. The induction motor is driven based on field-oriented control strategy. The Takagi-Sugeno type fuzzy supervisor predicts, depending on the variations of climatic variables such as irradiation and temperature, the optimum operating point for the photovoltaic source. The simulation results show the effectiveness of the proposed approach in transient and stationary regimes for different values of climatic variables.
Model predictive control using Euler method for switched-battery boost-multilevel inverter Ahmad Takiyuddin Abdullah; Sevia Mahdaliza Idrus; Shahrin Md Ayob
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1497-1508

Abstract

This paper presents the model predictive control (MPC) design using the backward Euler method for an 11-level switched-battery boost multilevel inverter (SBBMLI). The SBBMLI was proposed as the cost-effective solution for the communication power line in a high-speed rail (HSR) system. Initially, a generalized SBBMLI problem formulation was performed, and an open-loop simulation based on voltage and current mode controls was conducted. The finite control set-model predictive control (FCS-MPC) ability to track the reference sinusoidal output with low total harmonic distortion (THD) was then assessed as a performance criterion. Furthermore, the performance was assessed based on no-load and load disturbances. Finally, the results proved the ability of the proposed FCS-MPC to address non-linear dynamics, constraints, and its efficiency of implementation.
Dynamic programming-based control system development for advanced electric power drive Swapnil Ramesh Wadkar; Sudhir Madhav Patil; Maneetkumar R. Dhanvijay; Kiran Ashokrao Chaudhari; Atul Vasant Karanjkar
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1356-1367

Abstract

An efficient method for raising the effectiveness and performance of fuel cell electric vehicles (FCEVs) is the dynamic programming controller (DPC). By using real-time data to optimize the control inputs, FCEVs can achieve higher levels of efficiency and reduce their environmental impact. The DPC algorithm works by solving an optimization problem at each time step, based on the current state of the vehicle and its environment. The optimal control inputs are then applied to the vehicle to achieve the desired performance criteria. This paper presents the study that utilized MATLAB/Simulink to design, model, and simulate DPC for a FCEV. Controlling various components of the fuel cell (FC) with the optimum power requirement is needed for increasing the performance and mileage of the FCEV. It's important to use FC energy as effectively as possible. Having supervisory control over the FCEV's energy consumption and battery charging is necessary for it to produce this output at its best. To use the hydrogen efficiently, a control strategy is designed for energy management in FCEV. The designed control strategies are implemented through simulation using Simulink in MATLAB. The results show prominent performance of dynamic programming (DP) over rule-based controllers.
A state of the art a hybrid intelligent strategies of maximum power point tracking: a systematic contemporary Abbas Fakhri Shalal; Mohanad Aljanabi; Ali Najah Al-Shamani; Ahmed Hussein Duhis
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1768-1780

Abstract

Renewable energy sources are among the best substitute sources to fossil fuel due to it is very suitable for mitigating global warming; solar energy is considered the main causes of renewable energy, and solar photovoltaic (PV) generation systems have gained importance worldwide due to several characteristics, including cheap maintenance, low noise, and low fuel costs. However, one of the most difficult challenges facing solar energy systems is changing weather circumstances. The robust control approach stabilizes the PV system's output and maximizes harvested energy. In this study, several maximum power point tracking (MPPT) performances have been presented. Among them, artificial intelligence (AI) based on MPPT methods demonstrates the ability to capture the MPP point. There are several ways to apply AI to MPPT, and this paper presented various intelligent MPPT methods in detail with their benefits and drawbacks and comparison among them to select which technique is suitable and can be used to change weather conditions with horse optimization method (HOM) plus neural artificial system (NAS).

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