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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

Modeling, tuning, and validating of exciter and governor in combined-cycle power plants: a practical case study

10.11591/ijpeds.v16.i3.pp1645-1657
Saleh Baswaimi , Renuga Verayiah , Tan Yi Xu , Nagaraja Rupan Panneerchelvan , Aidil Azwin Zainul Abidin , Marayati Marsadek , Agileswari K. Ramasamy , Izham Zainal Abidin , W. Mohd Suhaimi Wan Jaafar
Exciter and governor systems are critical to regulating power output and maintaining stability in power systems. Despite their significance, there is a lack of practical methodologies that leverage real power plant data for modeling, tuning, and validation. This research paper seeks to fill this gap by presenting a methodology that utilizes a transfer function and control algorithms for tuning and validation. The proposed approach is demonstrated through a case study of a practical combined-cycle power plant in Malaysia. The control algorithm's effectiveness is verified through MATLAB and Simulink simulations. Post-tuning assessments confirm the method’s ability to accurately determine tunable control parameter settings, meeting system requirements while ensuring grid stability and reliability. This versatile approach can be applied to various power plant configurations, making it a valuable tool for optimizing operations.
Volume: 16
Issue: 3
Page: 1645-1657
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

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

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

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

An approach of battery adaptation in wireless sensor network with resource aware in extreme environmental area

10.11591/ijpeds.v16.i3.pp1812-1821
Jumadi Mabe Parenreng , Muhammad Reza Shiraj , Satria Gunawan Zain , Abdul Wahid
A wireless sensor network (WSN) is a distributed wireless system that employs sensor nodes to perform various tasks, including sensing, monitoring, data transmission, and delivering information to users via internet communication. Resource availability in WSNs is a critical factor influencing data delivery performance. One of the main challenges is the rapid depletion of resources, particularly batteries, which play a pivotal role in the system’s operational sustainability. This study evaluates the impact of battery adaptation through four testing scenarios. The results show that implementing battery adaptation significantly extends system lifespan compared to scenarios without adaptation. In the scenario without both a classification algorithm and adaptation, the system lasts approximately 270 minutes. When battery adaptation is applied without a classification algorithm, the lifespan increases to 330 minutes and 30 seconds. In contrast, the scenario using a classification algorithm without adaptation yields a lifespan of about 185 minutes, while combining the classification algorithm with adaptation extends it to approximately 252 minutes. The findings demonstrate that battery adaptation enhances the longevity and resource efficiency of WSN systems. However, the use of a classification algorithm tends to reduce operational time compared to scenarios that do not employ such algorithms.
Volume: 16
Issue: 3
Page: 1812-1821
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

Cancellation of periodic disturbances for dual start induction drives based on a novel robust adaptive control strategy

10.11591/ijpeds.v16.i3.pp1673-1686
Ngoc Thuy Pham , Phu Diep Nguyen
The disturbance cancellation has always been an important area that has received much attention, especially for the nonlinear drive systems as the dual start induction motor (DSIM). In this paper, a new robust adaptive hybrid strategy based on an improved variable-gain quasi-continuous third order sliding mode (VGQSTOSM) algorithm integrated with RC and a load torque disturbance estimator helps to reduce chattering, cancel the periodic and extended load disturbances, and enhance tracking performance effectively. By using third-order sliding mode with variable gain dependent on the magnitude of the sliding variable, this proposal aims to be adaptive. It provides higher gain when far from the sliding surface (is large), leading to faster convergence and lower gain when close to the sliding surface (is small), potentially reducing chattering further and decreasing control effort near the equilibrium. The robustness of the proposed controller is improved because the adaptive gain mechanism effectively compensates for uncertainties or disturbances. Furthermore, a plug-in RC is integrated into the improved high-order sliding mode structure (DRVGQSTOSM), and an estimated load torque disturbance value is also used to help identify and proactively eliminate disturbances. The system stability is assured using Lyapunov theory the virtual control vectors' outputs are chosen based on Lyapunov theory. Simulation results obtained using the MATLAB software confirm the tracking and harmonic disturbance rejection performance as well as the robustness of the proposed control strategy.
Volume: 16
Issue: 3
Page: 1673-1686
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

Empowering breastfeeding mothers: How self-directed learning boosts confidence-unveiling the two-round Delphi method

10.11591/ijphs.v14i3.25965
Dewi Ariani , Respati Suryanto Dradjat , Kumboyono Kumboyono , Lilik Zuhriyah
Promoting breastfeeding self-efficacy through self-directed learning requires behavior, goal setting, and self-reinforcement. This research aims to collect insights from health professionals on strategies for improving maternal confidence in breastfeeding using self-directed learning and existing knowledge. An in-depth exploration through a two-round Delphi method rooted in the self-efficacy theory of self-directed learning for breastfeeding mothers was conducted, involving expert input and an extensive literature review. Four key documents were identified, each undergoing rigorous expert rating to ensure quality. Six essential elements for health professionals to guide breastfeeding mothers were established, focusing on lactation physiology, successful initiation, confidence building, adversity management, cultural beliefs, and public breastfeeding. Three crucial topics, including prior knowledge, personal attributes, and autonomous processes, were designed to enhance self-efficacy through self-directed learning. In conclusion, the study emphasizes the vital role of health professionals in supporting mothers through comprehensive breastfeeding guidance and encouraging self-directed learning.
Volume: 14
Issue: 3
Page: 1256-1266
Publish at: 2025-09-01

Optimization of ANN-based DC voltage control using hybrid rain optimization algorithm for a transformerless high-gain boost converter

10.11591/ijpeds.v16.i3.pp1711-1720
Mohcine Byar , Abdelouahed Abounada
This paper introduces an adaptive voltage regulation technique for a transformerless high-gain boost converter (HGBC) integrated within standalone photovoltaic systems. A neural network controller is trained and fine-tuned using the rain optimization algorithm (ROA) to achieve improved dynamic behavior under variable solar conditions. The proposed ROA-ANN framework continuously updates the duty cycle to ensure output voltage stability in real time. Validation was carried out using MATLAB–OrCAD co-simulation under multiple scenarios. Comparative results highlight superior performance of the ROA-ANN controller in terms of convergence speed, overshoot minimization, and steady-state response, outperforming conventional PID and ANN-based methods.
Volume: 16
Issue: 3
Page: 1711-1720
Publish at: 2025-09-01

Low voltage fault ride-through operation of a photo-voltaic system connected utility grid by using dynamic voltage support scheme

10.11591/ijpeds.v16.i3.pp1608-1619
Satyanarayana Burada , Kottala Padma
This research suggests a control technique that makes use of a microgrid's energy storage and to enable low voltage ride through (LVRT) process with a flexible dynamic voltage support (DVS) system. First, the requirements for the microgrid's maximum DVS are stated, together with an explanation of how these requirements depend on the characteristics of the analogous network that the microgrid sees. In order to create a flexible DVS regardless of the changing system circumstances, reference signals for currents that are derived from maximum voltage tracking technique are suggested in this research. These signals take into account the challenges involved with real time parameter assessment in the context of transient voltage disruptions. Second, a control scheme is suggested to allow a microgrid's energy storage-based LVRT operation. Thirdly, a novel approach to energy storage sizing for LVRT operation is offered, taking into account the corresponding network characteristics, grid code requirements, and the rated current value of the power electronic converter. Real-time MATLAB simulations for low-voltage symmetrical faults are used to validate the suggested control technique.
Volume: 16
Issue: 3
Page: 1608-1619
Publish at: 2025-09-01

Synchronous generator system identification via dynamic simulation using PSS/E: Malaysian case

10.11591/ijpeds.v16.i3.pp1658-1672
Saleh Baswaimi , Renuga Verayiah , Tan Yi Xu , Nagaraja Rupan Panneerchelvan , Aidil Azwin Zainul Abidin , Marayati Marsadek , Agileswari K. Ramasamy , Izham Zainal Abidin , W. Mohd Suhaimi Wan Jaafar
The synchronous generator (SG) plays a crucial role in power systems by serving as a stable and reliable source of electrical energy. The performance of an SG hinges on its standard parameters, which can be derived through dynamic tests. This study introduces a method for determining the standard parameters of an SG from dynamic tests conducted via power system simulation for engineering (PSS/E). The proposed method entails conducting several key tests on the generator, including a direct-load rejection test, excitation removal test, quadrature-axis load rejection test, arbitrary axis load rejection test, and open-circuit saturation test. The results obtained from these tests are then utilized to calculate the standard parameters of the SG accurately. To validate the effectiveness of the method, simulation data from the SG, as well as the designed initial data, are utilized. Statistical analysis reveals that the maximum relative error is equal to or less than 2.7% of the design values for all standard parameters, emphasizing the robustness and accuracy of the proposed method. The methodology presented in this study can complement field or site measurements, as it enables the verification of system parameters through dynamic simulations.
Volume: 16
Issue: 3
Page: 1658-1672
Publish at: 2025-09-01

Optimizing slow-charging EV loads with a two-layer strategy to enhance split-phase voltage quality and mitigate issues in PDNs

10.11591/ijpeds.v16.i3.pp1472-1483
Attada Durga Prasad , Manickam Siva , Alla Srinivasa Reddy
Power distribution networks (PDN) were mostly affected by the voltage imbalances created by the slow charging of electric vehicles (EV), were there random load into the PDN system, causing split-phase voltage quality (SPVQ) issues. Hence, to mitigate the problems associated with EVs’ slow charge in distributed phases of the power system, a multi-layer charging strategy is proposed considering the following constraints in the system: voltage deviation (VD) and voltage harmonics (VH) in split phase (SP). Further multi-layer control is associated with an inner layer equipped with hybrid non-dominated sorting genetic algorithm (NSGA-II) to select the optimal phase for charging the EV and send it to the output layer where a SP current algorithm is utilized so that voltage quality can be fed in loop to inner layer so that iterations were performed to satisfy the convergence condition. Simulation results in MATLAB demonstrate a voltage unbalance (VU) reduction of up to 32.81%, a maximum VD reduction of 9.11%, and a VH reduction of 6.25% at key grid nodes. The proposed method significantly enhances PDN efficiency and maintains voltage quality within national standards across 1,000 to 5,000 EV connections. The generated results reflected the optimal improvement in SPVQ, and the harmonics content reduced further; PDN operational efficiency also improved to a greater extent.
Volume: 16
Issue: 3
Page: 1472-1483
Publish at: 2025-09-01
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