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28,451 Article Results

Predicting transmission losses using EEMD – SVR algorithm

10.11591/ijpeds.v16.i3.pp2122-2129
Hesti Tri Lestari , Catherine Olivia Sereati , Marsul Siregar , Karel Octavianus Bachri
This work introduces a predictive model for evaluating transmission losses in the Java-Bali electrical system using ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) techniques. Transmission losses, a critical aspect of energy efficiency, are affected by several operational aspects, such as load flow, energy composition, peak load, and meteorological factors such as transmission line temperature. Transmission losses data were decomposed into many intrinsic mode functions (IMFs) by EEMD, effectively capturing both high-frequency (short-term) and low-frequency (long-term) trends. The SVR algorithm, utilizing a radial basis function (RBF) kernel, was subsequently employed to predict the deconstructed IMFs, facilitating accurate predictions of transmission losses. The proposed EEMD-SVR model achieved a mean absolute error (MAE) of 5.43%, with the highest error observed during the period of abrupt load shifts. These results confirm the model’s strength in identifying long-term transmission loss patterns, making it suitable for system planning and operational forecasting. While the model exhibited high prediction accuracy, especially in recognizing long-term trends, it faced limitations in accurately predicting abrupt changes in transmission losses. Therefore, future improvements should aim to enhance responsiveness to sudden changes in the system dynamics. The result suggests that the EEMD SVR model can proficiently assist power system operators in monitoring and mitigating transmission losses.
Volume: 16
Issue: 3
Page: 2122-2129
Publish at: 2025-09-01

Improvement direct torque control of induction motor using robust intelligence artificial ANFIS speed controller

10.11591/ijpeds.v16.i3.pp1552-1565
Laoufi Abdelhaq , Chergui Moulay-Idriss , Soufiane Chekroun
This paper proposes a study aimed at improving the conventional direct torque control (DTC) technique applied to induction motors (IM). The primary aim is to reduce the harmonic distortions and fluctuations associated with the electrical current, flux variations, and generated torque, while ensuring accurate speed reference tracking and ensuring optimal dynamic performance of the drive, especially under variable speed conditions. To achieve this, we introduce an intelligent control system that utilizes a hybrid neuro-fuzzy inference model (ANFIS), through the application of the back propagation method. The DTC-ANFIS technique is compared with the traditional DTC-PI method and simulated using MATLAB/Simulink in different scenarios. The obtained results reveal a significant improvement in performance over DTC-PI, with superior results over a wide speed range.
Volume: 16
Issue: 3
Page: 1552-1565
Publish at: 2025-09-01

Advances in medical power electronics: applications and challenges

10.11591/ijpeds.v16.i3.pp1983-1990
Hamza Abu Owida , Jamal I. Al-Nabulsi , Nidal Turab , Muhammad Al-Ayyad
Power electronics plays a crucial role in modern medical applications by providing efficient power management, conversion, and regulation across a wide range of devices. In high-power systems, such as medical imaging equipment, power electronics ensure precise control, stable operation, and optimal performance, which are essential for accurate diagnostic imaging. On the other hand, in low-power devices such as wearable health monitors and implantable medical devices, power electronics focus on enhancing energy efficiency and miniaturization. This is vital for extending battery life, reducing the need for frequent recharging or replacement, and improving patient comfort and mobility. This review examines the role of power electronics in diverse medical applications, highlighting its importance in enabling stable performance in critical life-support systems, therapeutic devices, and portable health monitors. Key technologies and power management integrated circuits are explored for their contribution to improving the efficiency, reliability, and longevity of medical devices. The review also addresses significant challenges, including miniaturization, energy efficiency, and regulatory compliance. Future trends such as the development of advanced semiconductor materials, innovations in energy harvesting techniques, and wireless power transfer technologies are also discussed. These advancements are expected to revolutionize the field, driving the next generation of medical devices and shaping the future of healthcare technology.
Volume: 16
Issue: 3
Page: 1983-1990
Publish at: 2025-09-01

Predictive machine learning for smart grid demand response and efficiency optimization

10.11591/ijpeds.v16.i3.pp1628-1636
J. C. Vinitha , J. Sumithra , M. J. Suganya , P. Aileen Sonia Dhas , Balaji Ramalingam , Sivakumar Pushparaj
This paper explores the evolution of smart grids (SGs) and how they enable consumers to schedule household appliances based on demand response programs (DRs) provided by distribution system operators (DSOs). This study looks at and compares four distinct regression models: linear regression, random forest regressor, gradient boosting regressor, and support vector regressor. This is being done because more and more people are using machine learning (ML) methods to make this process better. The models are trained and tested using a dataset that includes a variety of parameters, such as humidity, temperature, and the amount of power used by appliances. Mean squared error (MSE) and R-squared values are two important performance measures that are used to judge these models and see how well they can make predictions. These results reveal that the gradient boosting regressor was the most accurate model for figuring out how much energy smart homes use. This algorithm could be a great tool for better managing energy use because it can figure out the complicated connections between the things that are input and the amount of energy that appliances use. This study makes a big difference in the creation of strong regression models by emphasizing how important it is to be accurate when making predictions. This, in turn, helps to enhance energy sustainability and economic stability in smart home environments.
Volume: 16
Issue: 3
Page: 1628-1636
Publish at: 2025-09-01

Inverter transient response improvement using grey wolf optimizer for type-2 fuzzy control in HVDC transmission link

10.11591/ijpeds.v16.i3.pp2130-2142
I Made Ginarsa , Agung Budi Muljono , I Made Ari Nrartha , Ni Made Seniari , Sultan Sultan , Osea Zebua
High voltage direct current (HVDC) on transmission-link becomes a new prominent technology in recent years. The HVDC is applied to transmit amount of electrical energy from power plant to consumers. This method makes reactive power losses on transmission devices decrease significantly and stability level of generator increases. However, inverter HVDC transmission system can produce slow and high inverter transient current (ITC) response at high value of the up-ramp rate. This ITC phenomenon can be serious problem at starting time. So grey wolf algorithm is proposed to optimize input-output parameters of interval type-2 fuzzy control (IT2FC) in inverter-side HVDC. The proposed control performance’s is assessed by integral time squared error (ITSE) and peak overshoot (Mp) approaches. Simulation results show that small ITSE and low Mp of transient response are given by the IT2FC. The IT2FC is successful applied on inverter HVDC with better results compared to conventional PI control scheme.
Volume: 16
Issue: 3
Page: 2130-2142
Publish at: 2025-09-01

A new approach for optimal sizing and allocation of distributed generation in power grids

10.11591/ijpeds.v16.i3.pp1598-1607
Hudefah Alkashashneh , Ayman Agha , Mohammed Baniyounis , Wasseem Al-Rousan
This paper presents a methodology for optimizing the allocation and sizing of distributed generators (DG) in electrical systems, aiming to minimize active power losses on transmission lines and maintain bus voltages within permissible limits. The approach consists of two stages. First, a sensitivity based analysis is used to identify the optimal candidate bus or buses for DG placement. In the second stage, a new random number generation method is applied to determine the optimal DG sizing. Moreover, a ranking for the optimal locations and sizes is given in case the optimal location is unavailable in real-world scenarios. The proposed methodology is demonstrated through a straightforward algorithm and tested on the IEEE 14-bus and IEEE 30-bus networks. Numerical simulations in MATLAB illustrate the effectiveness of the proposed approach in finding the optimal allocation of DG and the amount of active power to be allocated at the candidate buses, considering the inequality constraints regarding voltage limits and DG allowable power. The paper concludes with results, discussions, and recommendations derived from the proposed approach.
Volume: 16
Issue: 3
Page: 1598-1607
Publish at: 2025-09-01

Micro short circuit fault diagnosis in Li-ion cell

10.11591/ijpeds.v16.i3.pp2103-2111
S. Gomathy , Boopathi Dhanasekaran , Sivabalakrishnan Ramasamy , Radha Jayaram , Sabarimuthu Muthusamy , A. T. Sankara Subramanian
Micro short circuits (MSCs) in lithium-ion battery cells are a critical safety concern, potentially leading to thermal runaway, internal short circuits, overheating, and battery degradation. Compared to normal cells, MSC fault cells exhibit reduced capacity with each charge-discharge cycle and an increasing state of charge (SOC) deviation over time. To differentiate normal cells from MSC fault cells, a fault diagnosis method based on remaining charge capacity (RCC) estimation is proposed. After each charge discharge cycle, the cell’s RCC is compared to a safe threshold value. The method uses the charge cell voltage curve (CCVC) of a fully charged reference cell to estimate RCC via standard CCVC hypothetical conversion. This approach’s accuracy is validated in constant power and constant current charging scenarios. MSC leakage current is calculated by incrementing RCC after each charge, and then converted to MSC resistance. A MATLAB/Simulink model of a battery pack with an MSC fault was developed to test the method across various charge cut-off voltages. The diagnostic procedure’s applicability to ageing cells, constant power, and multi-step charging is further confirmed through experiments with external resistance, enhancing MSC detection before thermal runaway becomes unmanageable.
Volume: 16
Issue: 3
Page: 2103-2111
Publish at: 2025-09-01

Resonant converter for fast-charging applications

10.11591/ijpeds.v16.i3.pp1832-1839
Remala Geshma Kumari , Narahari Krishna Kumari , Kankipati Shravya
Resonant converters (RCs) are gaining attention from the research community due to their significant contributions to the architecture of electric vehicle (EV) charging infrastructure. The primary part of RC is responsible for enabling constant-current (CC) charging, which helps lower inrush current, decrease losses, and improve efficiency. While the load current stays constant during charging using the CC approach, the source current grows linearly with charging time. However, pulling a high source current increases the rating of the inverter switches, which stresses them, raises their temperature, increases heat sink demand, and causes conduction loss—all of which are undesirable. Consequently, the rated CC is provided by the P2 topology of RC, which has a lower peak current source than other topologies and will improve charger performance. However, this assertion must be verified by mathematical modeling, design with theoretical calculations, specifications, and MATLAB simulation before execution. By providing a constant load current of 5 A at a DC source voltage of 200 V, the P2 RC and the conventional LCL RC are designed to compare source current values.
Volume: 16
Issue: 3
Page: 1832-1839
Publish at: 2025-09-01

Optimization of two-stage DTMOS operational transconductance amplifier with Firefly algorithm

10.11591/ijpeds.v16.i3.pp1417-1428
Udari Gnaneshwara Chary , Swathi Mummadi , Kakarla Hari Kishore
This paper presents a methodology for optimizing dynamic threshold MOSFET (DTMOS) two-stage operational transconductance amplifiers (OTAs) tailored for biomedical applications through the utilization of the Firefly algorithm. The optimization process focuses on enhancing key performance metrics such as gain, bandwidth, and power efficiency, which are critical for biomedical signal processing, neural interfaces, and wearable healthcare devices. The methodology encompasses circuit architecture definition, Firefly algorithm implementation, fitness evaluation, and result analysis. The optimization results reveal a significant enhancement in performance metrics. Specifically, the number of transistors in the design is 25. The initial overall gain was 76.65 V/V, with a power efficiency (µ) of 1.6. After optimization, the overall gain was significantly improved to 84.029 dB using the Firefly algorithm, demonstrating superior performance compared to existing algorithms. The power efficiency (µ) was also enhanced to 1.702, underscoring the efficiency improvements achieved through optimization. Simulation results and statistical analysis confirm that the Firefly algorithm effectively achieves optimal configurations, improving the robustness of OTA designs against parameter variations. These enhancements validate the algorithm's efficacy in addressing power-performance trade-offs and its suitability for diverse biomedical applications. Physical prototyping of the optimized design further demonstrates real-world functionality, underscoring its practical applicability.
Volume: 16
Issue: 3
Page: 1417-1428
Publish at: 2025-09-01

Digital twin-based performance evaluation of a photovoltaic system: A real-time monitoring and optimization framework

10.11591/ijpeds.v16.i3.pp2072-2081
Mustafa Fadel , Fajer M. Alelaj
The digital twin (DT) technology implementation in photovoltaic (PV) systems provides an innovative approach to real-time performance monitoring and predictive maintenance. In this paper, an end-to-end DT framework for real-time performance analysis, fault detection, and optimization of a 250 W PV system is proposed. A physics-based equation and AI-based prediction hybrid DT model is developed through MATLAB/Simulink, trained from real data acquired by means of a testbed. The DT simulates the dynamic physical PV system behavior and adjusts itself using self-correcting algorithms to enhance precision in prediction and forecast power output at high fidelity. Results indicate that the DT gives the true response of the PV system with very small differences attributable to model approximations and sensor faults, 95% error minimization after compensation, and a root mean square error (RMSE) of 2.8 W, indicating its applicability for real-time monitoring and predictive main-maintenance. The work here focuses on the feasibility of applying DTs towards the autonomous optimization of distributed renewable energy systems.
Volume: 16
Issue: 3
Page: 2072-2081
Publish at: 2025-09-01

Smart energy management in renewable microgrids: integrating IoT with TSK-fuzzy logic controllers

10.11591/ijpeds.v16.i3.pp1620-1627
Moazzam Haidari , Vivek Kumar
Hybrid microgrids powered by renewable energy sources are gaining popularity globally. Photovoltaic (PV) and permanent magnet synchronous generator (PMSG)-based wind energy systems are widely used due to their ease of installation. However, wind and solar energy are unpredictable, leading to fluctuating power generation. Simultaneously, load demand varies randomly, making it necessary to integrate storage devices to maintain a balance between generation and consumption. To enhance system economy, a small battery is combined with a hydrogen-based fuel cell and electrolyzer for efficient energy storage and management. A robust energy management system (EMS) is critical to ensure power quality and reliability across all microgrid components. Maximum power point trackers (MPPTs) are employed to maximize renewable energy utilization. Frequency stability and ensuring power balance is important in autonomous microgrids, especially during rapid load or source variations. This paper presents a novel fuzzy rule-driven Takagi-Sugeno-Kang (TSK) controller for the EMS, ensuring fast, precise responses and improved microgrid reliability.
Volume: 16
Issue: 3
Page: 1620-1627
Publish at: 2025-09-01

Modeling and simulation of klystron-modulator for linear accelerators in PRTA

10.11591/ijpeds.v16.i3.pp1822-1831
Wijono Wijono , Dwi Handoko Arthanto , Galih Setiaji , Angga Dwi Saputra , Taufik Taufik , Andang Widi Harto
Approximately 70% of commercial industries worldwide use electron accelerator technology for various irradiation processes. The advantages of irradiation processes compared to thermal and chemical processes are higher output levels, reduced energy consumption, less environmental pollution, and producing superior product quality and having unique characteristics that cannot be imitated by other methods. Research Center for Accelerator Technology (PRTA), BRIN, Indonesia is developing standing wave LINAC (SWL) for food irradiation applications at S-band frequencies (±2856 MHz), electron energy of 6-18 MeV, and an average beam power of 20 kW. This paper aims to model, simulate, and analyze the klystron modulator in the RF linear accelerator (LINAC). The klystron modulator is the main component of the RF LINAC, which functions to supply klystron power with the order of megawatt peak DC, so that the klystron can amplify the low-level RF signal from the RF driver into a high-power RF signal with a power of 2-6 MW peak. The klystron modulator modeling is carried out based on mathematical modeling, then simulated using LTspice to analyze the system performance of the klystron modulator. The results of the klystron modulator modeling simulation show stable system performance and dynamic response. So that it meets the specifications of the 6-18 MeV SWL LINAC being developed by PRTA-BRIN.
Volume: 16
Issue: 3
Page: 1822-1831
Publish at: 2025-09-01

Design and optimization of hybrid microgrid renewable energy system for electricity sustainability in remote area

10.11591/ijpeds.v16.i3.pp2063-2071
Theresa Chinyere Ogbuanya , Taiwo Felix Adebayo
Off-grid hybrid electrical systems have become a viable option for sustainable energy solutions, meeting the energy supply needs of rural communities. These systems use a broad approach to tackle sustainability, dependability, and environmental protection problems. The suggested hybrid system combines battery storage, biogas generators, and solar photovoltaic (PV) to provide a reliable and strong energy source for Ivoko Village in Enugu State using particle swarm optimization (PSO) and HOMER Pro Software. The paper compares three different configurations of sustainable power systems (HRES) to determine the best architecture that is suitable for rural areas. The result shows that case-1 (biogas/PV/bat) is the best option, with net present cost (NPC) and cost of energy (COE) values of $1,225,914 and 0.2865$/kWh, respectively. The results show that the PSO-based hybrid power system is more cost-effective than the HOMER-based optimizer. The NPC and lower COE for meeting peak demands emphasize the increasing role of biogas system generators as a cost-effective local power source. This highlights the PSO's potential in maximizing hybrid renewable power systems for rural areas, offering a financially viable and sustainable energy solution.
Volume: 16
Issue: 3
Page: 2063-2071
Publish at: 2025-09-01

Improving electrical energy efficiency through hydroelectric power and turbine optimization at the El Oued water demineralization plant in Algeria

10.11591/ijpeds.v16.i3.pp1881-1896
Khaled Miloudi , Ali Medjghou , Ala Eddine Djokhrab , Mosbah Laouamer , Souheib Remha , Yacine Aoun
This paper presents an investigation into the energy potential of the Albian aquifer in the Algerian Sahara at the El Oued water demineralization plant, focusing on its capacity to generate electrical power due to its high-pressure and high-temperature water reserves. We designed and implemented a turbine-generator system to convert hydraulic energy into electricity, achieving an average annual energy output of 1,804,560 kWh, which translates to a financial gain of approximately 345,888,600 DZD per year from energy savings. The selection of a Francis turbine was justified based on its efficiency, which ranges from 90% to 95%, and the system design was simulated using MATLAB-Simulink, demonstrating its robustness and effectiveness in managing the electrical network parameters. Our economic analysis indicates a high return on investment, confirming the feasibility of utilizing the Albian aquifer as a strategic asset for clean and reliable energy production in the region.
Volume: 16
Issue: 3
Page: 1881-1896
Publish at: 2025-09-01

Improved hybrid DTC technology for eCAR 4-wheels drive

10.11591/ijpeds.v16.i3.pp1566-1585
Njock Batake Emmanuel Eric , Nyobe Yome Jean Maurice , Ngoma Jean Pierre , Ndoumbé Matéké Max
This article deals with the design of a hybrid controller (HyC). It combines fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS). It is combined with direct torque control (DTC). This HyC-DTC combination is designed to improve the technical performance of a 04-wheel drive electric vehicle (EV). A stress test is identically applied to the DTC combined with the FL (FDTC) and to the HyC-DTC in order to certify the suitability of this new control following a cross-validation. This is based on dynamic stability criteria (overshoot, rise time, accuracy), analysis of torque and flux oscillations, and the EV's robustness symbol. The EV's magnetic quantities are managed by a master-slave module (VMSC). Simulations are carried out using MATLAB/Simulink software. The HyC-DTC achieves near-zero accuracy like the FDTC, with overshoot around 0.2% less than the FDTC, and torque oscillation amplitude around 4 times less than the FDTC. However, its rise time is 0.045% greater than that of the FDTC. It is therefore slower, but more precise and suitable for EV transmission systems in terms of safety and comfort.
Volume: 16
Issue: 3
Page: 1566-1585
Publish at: 2025-09-01
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