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29,758 Article Results

Enhanced adaptive reconfiguration for optimizing power generation and switching efficiency in PV arrays under PSC

10.11591/ijpeds.v17.i1.pp777-785
D. Manimegalai , Kandadai Nagaratnam Srinivas , Gayathri Monicka Subarnan
Photovoltaic (PV) arrays suffer significant power losses under partial shading conditions (PSC), which can degrade system performance. This paper proposes a novel weighted objective function that balances power output maximization with switching action minimization during dynamic PV array reconfiguration. An enhanced firebug swarm optimization (FSO) algorithm is employed to optimize this function efficiently. Simulation results under five shading patterns demonstrate approximately 6% improvement in power output over conventional methods, while also reducing the number of switch operations. The proposed approach enhances energy yield and extends device lifespan, offering a robust solution for real-time PV optimization under PSC.
Volume: 17
Issue: 1
Page: 777-785
Publish at: 2026-03-01

Development of numerical model-based photovoltaic emulator for half-cut cell PV panel with multiple peaks output characteristics curve emulation capability

10.11591/ijpeds.v17.i1.pp343-358
Jordan S. Z. Lee , Jia Shun Koh , Rodney H. G. Tan , Nadia M. L. Tan , Thanikanti Sudhakar Babu
This study introduces a photovoltaic (PV) emulator focusing on a developed numerical model specifically for half-cut cell PV panels under partial shading conditions (PSCs), addressing a gap in research focused on full-cell models. The emulator uses a DC-DC buck converter and PI control to accurately replicate half-cut cell PV panel characteristics. A cost-effective hardware prototype validated the model's effectiveness in emulating multi-peak PV behavior under dynamic PSCs with up to three peaks and user-defined shading. This flexible and affordable platform enables efficient testing of MPPT algorithms and grid integration for PV systems using increasingly prevalent half-cut cell technology. Simulation results show high accuracy, with MAPE in power as low as 0.175% under uniform irradiance conditions and less than 0.302% under multi-peaks PSCs. Hardware validation confirms reliability with low MAPE in the power of 0.499% under uniform conditions and below 0.614% multi-peak PSCs, demonstrating the developed half-cut cell PV panel numerical model's accuracy in reproducing dynamic shading effects for renewable energy research.
Volume: 17
Issue: 1
Page: 343-358
Publish at: 2026-03-01

A three isolated port DC/DC converter for an energy storage system for renewable energy applications

10.11591/ijpeds.v17.i1.pp533-552
Faruk Ahmeti , Dimitar Arnaudov , Sabrije Osmanaj
The use of renewable energy sources like solar photovoltaic, wind, and fuel cells is gaining popularity due to growing environmental awareness, technological advancements, and declining production costs. Power electronic converters are usually used to convert the power from renewable sources to match the load demand and grid requirements. Among these, DC–DC converters are essential for improving system functionality and power density, especially in low-voltage renewable systems that require high voltage gain. This paper presents a systematic evaluation of five advanced DC-DC converter topologies: multi-port DC, boost multiport interleaved step-up, isolated bidirectional, voltage/current fed, and general resonant focusing on their structural complexity, component count, and potential application scenarios. In addition, a novel high-gain three-port resonant A DC-DC converter is proposed, incorporating galvanic isolation via a three-winding high-frequency transformer. The converter adopts a half-bridge resonant inverter and rectifier-based load port, resulting in a compact and cost-effective solution. A detailed analysis of the converter's operation, design considerations, and control strategy is conducted using PLECS simulation. Furthermore, an experimental setup is developed to validate the converter’s practical feasibility. The setup schematic and comprehensive comparative tables are included to support the evaluation and highlight the proposed design’s capabilities.
Volume: 17
Issue: 1
Page: 533-552
Publish at: 2026-03-01

Solar power forecasting using a SARIMA approach for Indonesia's grid integration

10.11591/ijpeds.v17.i1.pp293-302
Ricky Maulana , Syafii Syafii , Aulia Aulia
Indonesia’s transition toward a renewable energy-dominated power grid is progressing to meet increasing energy demands while reducing dependence on fossil fuels. According to the National Energy General Plan, their goal is to have 23% of the energy mix come from renewables by 2025 and 31% by 2050. Accurate forecasting of photovoltaic (PV) power output is crucial to address the intermittent nature of solar energy and ensure grid stability. A seasonal autoregressive integrated moving average (SARIMA) model was developed to estimate day-ahead photovoltaic power output in Padang City, Indonesia. Using NASA solar irradiance data from March 1-31, 2024, the SARIMA(1,0,1)(4,0,3)24 model achieved high accuracy with an NRMSE of 4.19%. To evaluate its performance, a comparative evaluation was conducted between the SARIMA model and two machine learning methods, namely artificial neural network (ANN) and long short-term memory (LSTM), in which SARIMA achieved the lowest forecasting error. These findings indicate that SARIMA remains an effective and interpretable statistical method for short-term PV forecasting, supporting reliable energy planning and power grid operations towards Indonesia's renewable energy goals.
Volume: 17
Issue: 1
Page: 293-302
Publish at: 2026-03-01

Performance enhancement of photovoltaic systems using hybrid LSTM-CNN solar forecasting integrated with P&O MPPT

10.11591/ijpeds.v17.i1.pp696-708
Sara Fennane , Houda Kacimi , Hamza Mabchour , Fatehi ALtalqi , Adil Echchelh
The increasing penetration of photovoltaic (PV) systems in smart grids highlights the need for reliable solutions to mitigate the inherent intermittency of solar energy. Short-term variability in solar irradiance remains a critical challenge for stable grid operation and efficient PV energy management. This paper proposes an integrated forecasting-control framework that combines short-term global horizontal irradiance (GHI) prediction with a conventional P&O MPPT strategy to enhance PV system performance. A hybrid LSTM-CNN architecture is developed to forecast one-step-ahead GHI under the semi-arid climatic conditions of Dakhla, Morocco, a region characterized by high solar potential and pronounced irradiance fluctuations. The forecasting model is validated using measured irradiance data from the National Renewable Energy Laboratory (NREL) via the National Solar Radiation Database (NSRDB). Predicted irradiance is then used to improve PV power estimation and support predictive maximum power point tracking (MPPT) operation. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed framework achieves accurate GHI forecasting, faster MPPT convergence, reduced steady-state oscillations, and improved PV power stability under rapidly changing irradiance. The proposed approach provides a practical and computationally efficient solution for enhancing the dynamic response and energy extraction efficiency of PV systems in smart grid applications.
Volume: 17
Issue: 1
Page: 696-708
Publish at: 2026-03-01

Efficiency of squirrel-cage induction motors with copper and aluminum rotors

10.11591/ijpeds.v17.i1.pp223-237
Ines Bula Bunjaku , Edin Bula
This study presents a method for estimating efficiency in three-phase squirrel-cage induction motors with copper and aluminum rotor cages. A detailed two-dimensional transient finite-element model of a 1.25 kW motor was created and analyzed under rated conditions (500 V, 50 Hz, 990 rpm, 75 °C) to determine torque, slip, losses, and efficiency. Finite-element results confirmed the copper rotor's advantage, with 11.0% higher efficiency (85.1% compared to 76.7%) and 37.5% lower rotor-cage losses (80 W compared to 128 W) compared to aluminum. For rapid efficiency prediction, both Mamdani-type fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) models were developed using simulation data. The fuzzy system showed a maximum deviation of 0.8% for the copper rotor, while the neuro-fuzzy approach achieved effective nonlinear mapping for both rotor types with R² = 0.872 against finite-element benchmarks. Sensitivity tests with ±0.3% slip and ±15 W loss variations maintained estimation errors below 2.5%. This combined simulation and intelligent system methodology enables practical efficiency evaluation and rotor material comparison for motor condition assessment and industrial energy management.
Volume: 17
Issue: 1
Page: 223-237
Publish at: 2026-03-01

Performance evaluation of dynamic voltage restorer using bidirectional impedance converter with UCAP

10.11591/ijpeds.v17.i1.pp465-475
A. Anitha , K. C. R. Nisha
With the involvement of renewable energy sources, plug-in hybrid automobiles, and fault occurrence, power quality has degraded nowadays. The most effective device utilized in distribution systems to enhance power quality is the dynamic voltage restorer (DVR). For deep sags, DVR with storage topology is more beneficial, although it has challenges with converter and storage element rating. To address this, various converters and energy storage elements like ultracapacitors are reviewed. In this paper, a DVR with an ultra-capacitor (UCAP) using an impedance bidirectional converter is simulated, and power quality indices are compared with VSI-BDC. The simulation result reflects the enhanced capability of the suggested DVR in a wide range of operations, improved power quality indices, and its effectiveness in swell conditions. The control of DC link voltage with PI and model predictive control (MPC) were simulated and compared.
Volume: 17
Issue: 1
Page: 465-475
Publish at: 2026-03-01

Modulation and performance analysis of two-wheeler electric vehicle

10.11591/ijape.v15.i1.pp186-194
Debani Prasad Mishra , Rudranarayan Senapati , Pavan Kumar , Lakshay Bhardwaj , Surender Reddy Salkuti
When compared to traditional cars, electric vehicles (EVs) have less pollution, better fuel efficiency, and are better for the environment. This essay explores the evolution of EVs in great detail, emphasizing their vital role in lowering CO2 emissions and promoting sustainability. It builds a dynamic model for EVs using MATLAB/Simulink, which explains the state of charge (SOC) and range prediction. The study emphasizes the importance of EVs in promoting a sustainable future by thoroughly covering design details, modeling, and a scientific methodology. Through the use of modeling to clarify technical aspects and highlight the significance of EV adoption, this study highlights the vital role that EVs play in reducing environmental impact and advancing environmentally friendly transportation. It highlights EVs' potential to revolutionize the automobile sector while promoting cleaner modes of transportation. It offers a thorough overview of EV production and usage and fervently promotes their wider acceptance as a means of laying the groundwork for a more sustainable and clean future.
Volume: 15
Issue: 1
Page: 186-194
Publish at: 2026-03-01

ANFIS-MPPT based PMSG-wind turbine interfaced with water pumping and battery management systems for optimal power flow and energy management

10.11591/ijape.v15.i1.pp141-152
Saritha Kandukuri , Ram Dulare Nirala , Sivaprasad Kollati , Tata Himaja , Durga Bhavani Adireddy
This paper presents the adaptive neuro-fuzzy inference system-maximum power point tracking (ANFIS-MPPT) approach for optimizing power flow in a water system powered by a permanent magnet synchronous generator (PMSG)-wind turbine. The system uses a PMSG-based wind energy conversion system (WECS) with an ANFIS for MPPT, enabling efficient power extraction under variable wind conditions. A bidirectional SEPIC-Zeta converter interfaces a battery energy storage system (BESS) to regulate the DC-bus voltage and maintain continuous power supply to a three-phase induction motor driving the water pump. An artificial neural network (ANN)-based controller is used to manage the charging and discharging of the battery based on real-time voltage deviation. The entire system, including wind turbine, PMSG, converters, and intelligent control algorithms, is modeled and simulated in MATLAB/Simulink. Comparative analysis with conventional MPPT techniques highlights the superior performance of the proposed hybrid ANFIS-based control in terms of power flow regulation, voltage stability, and operational reliability. The results confirm that the proposed approach significantly enhances energy management and system resilience, making it suitable for standalone or remote water pumping applications powered by renewable energy sources.
Volume: 15
Issue: 1
Page: 141-152
Publish at: 2026-03-01

Hydrothermal synthesis and defect-driven optical characterization of CdS nanoparticles for semiconductor and solar applications

10.11591/ijape.v15.i1.pp440-448
Deepti Bhargava , R. K. N. R. Manepalli , M. C. Rao , P. Venkata Ramana Rao , N. S. Subba Rao , A. Narendra Babu , P. Sree Brahmanandam
Nanoparticles (NPs) play a crucial role in advancing technology, particularly by enhancing the performance of energy storage in semiconductor applications. The synthesis of NPs with reduced particle size and increased surface area, along with a higher number of active sites, facilitates improved ion diffusion, making them highly suitable for such applications. Various methods have been employed to reduce the size of NPs, depending on factors such as purity and controlled composition. The present study focuses on controlling both the size and composition of cadmium sulfide (CdS) NPs, aiming to achieve a high surface-to-volume ratio. These NPs were synthesized using a hydrothermal method in a high-pressure autoclave. The evaluation of the synthesized inorganic CdS-NPs for technological applications requires experimental validation of their characteristics, including particle size, energy band gap, thermal stability, temperature response, as well as optical and electronic properties. The results obtained using the proposed methods reveal a bandgap of 2.28 eV, a hexagonal wurtzite structure with an average crystallite size of 10.26 nm, reduced effective mass, and an intense absorption peak at a higher wavelength. These characteristics indicate that the synthesized CdS nanoparticles are suitable for various applications, including high-power semiconductors, solar energy harvesting, optoelectronic devices, and materials for energy and electrical engineering.
Volume: 15
Issue: 1
Page: 440-448
Publish at: 2026-03-01

Robust SOC estimation for lithium-ion batteries under faulty charging scenarios using sliding mode observer techniques

10.11591/ijape.v15.i1.pp46-58
Soulef Mahiddine , Abdelghani Djeddi , Dib Djalel
With the growing demand for electric vehicles, embedded electronics, and renewable energy applications, lithium-ion batteries have become an essential component in modern energy storage systems. Accurate state of charge (SOC) estimation is crucial for ensuring battery reliability, longevity, and safety, particularly under faulty charging conditions—a challenge where many conventional estimation techniques fall short due to model limitations or lack of robustness. In this study, we propose an advanced SOC estimation approach based on a sliding mode observer (SMO) integrated with a third-order equivalent circuit model (ECM). Unlike conventional methods, which either focus on SOC estimation without considering battery voltage or apply SMO techniques only to second-order models, our approach enhances estimation accuracy by incorporating a higher-order model that better captures the complex battery dynamics. The proposed methodology is tested under both normal and faulty charging conditions, demonstrating superior performance in estimating both SOC and terminal voltage over extended periods. The simulation results confirm the robustness of the method, with accurate SOC tracking even in the presence of charging current faults, making it a viable solution for real-world applications in battery management systems (BMS). This work contributes to improving fault-tolerant SOC estimation strategies, advancing the development of safer and more efficient energy storage technologies.
Volume: 15
Issue: 1
Page: 46-58
Publish at: 2026-03-01

Blade number and angle effect the archimedes spiral wind turbine performance

10.11591/ijape.v15.i1.pp393-402
Rosadila Febritasari , Muhammad Ibnul Abidin
The efficiency and performance of Archimedes spiral wind turbine (ASWT) are affected by the design and number of turbine blades which can convert the kinetic energy of the wind into mechanical energy to turn a generator that can produce electricity as much as possible in low wind speed. This study aims to obtain the optimal ASWT design in low wind speed in terms of aerodynamic performance. The method is conducted by numerically computational fluid dynamics (CFD) simulation on the fixed-opening angle and the blades number variations. The results show that the smallest C_D value is -2.18 at the 65° of opening angle, the largest C_L value at the 45° of opening angle is 0.37, and the largest C_M value is 0.61 at the 65° of opening angle and 4 blades. Therefore, it can be concluded that the Archimedes wind turbine with 4 blades and 65° pitch is the optimal.
Volume: 15
Issue: 1
Page: 393-402
Publish at: 2026-03-01

Trapezoidal PWM scheme for voltage gain inverter

10.11591/ijape.v15.i1.pp90-97
Harikrishna Naraboyana , I. Kumaraswamy
The trapezoidal modulating wave-based high voltage gain 9-level inverter (HVG9LI) addresses significant difficulties related to the growing usage of capacitors, DC sources, and semiconductor switches. The proposed HVG9LI generates a nine-level resultant voltage with few components, exhibiting the capacity to double the output voltage gain. Furthermore, the HVG9LI utilizes a trapezoidal modulating wave and variable frequency carrier (TM-VFC) pulse width modulation method to increase the resulting voltage and enhance the voltage output quality. The performance and practicability of the HVG9LI with TM-VFC are evaluated across several modulation techniques and indices implemented by using MATLAB/SIMULINK and tested experimentally.
Volume: 15
Issue: 1
Page: 90-97
Publish at: 2026-03-01

Extending battery life and reducing charging costs in electric vehicles through converter selection for on-board chargers

10.11591/ijape.v15.i1.pp14-22
Jangam Kishore Babu , Ganney Poorna Chandra Rao , Puvvula Venkata Rama Krishna , Swathi Karike , Sailaja Kethireddy , Sareddy Venkata Rami Reddy , B. Nagi Reddy , Rekha Rangam
The electric vehicle (EV) sector is among the quickly expanding industries today. Global commitment to reducing pollution levels promotes interest in EVs. Fuel combustion engines emit around 10% of the globe's greenhouse gas emissions, which exacerbate the greenhouse effect. The emissions from electric vehicles are 17–27% less than those from internal combustion engines. The short battery life, high cost of charging, and lack of charging stations are some disadvantages of electric vehicles. The goal of this study is to suggest the ideal converter for the on-board charger (OBC), one that can extend battery life by lowering charging current at extremes of state of charge (SOC) and lower charging costs by increasing power factor (PF). The current control range than the isolated converter using transformers. Lastly, an analysis of the MATLAB/Simulink output findings is conducted to verify the effectiveness of the suggested OBC design with a non-isolated converter.
Volume: 15
Issue: 1
Page: 14-22
Publish at: 2026-03-01

Artificial neural network-optimized bridgeless Landsman converter for enhanced power factor correction in electric vehicle applications

10.11591/ijape.v15.i1.pp238-247
Podila Purna Chandra Rao , Radhakrishnan Anandhakumar , T. Vijay Muni , L. Shanmukha Rao
Electric vehicles (EVs) are gaining popularity globally due to their energy-efficient battery storage systems, low carbon emissions, and eco-friendly operation. By transforming both the transportation and electrical sectors, EVs could create a synergistic relationship that reduces fossil fuel use and improves renewable energy integration. However, this convergence emphasizes the necessity for appropriate power factor correction (PFC) methods, especially in EV battery charging systems, to alleviate supply-end PQ concerns. Use of a bridgeless Landsman converter (BLC), noted for its efficiency and link voltage monitoring, is innovative in this research. A proportional-integral (PI) controller tuned by an artificial neural network (ANN) improves prediction and classification, especially response time. The ANN-based PI controller optimises system performance in real time using adaptive control. Using a hysteresis controller attached to a pulse width modulation (PWM) generator regulates the converter's steady-state switching frequency for accurate and consistent output. The proposed approach reduces harmonic distortions and improves operating efficiency. This comprehensive architecture improves power factor and addresses significant PQ concerns in EV charging infrastructure. Integrating improved control tactics and converter design shows that this approach may support electric car technology developments. MATLAB simulations show that power factor correction (PFC) charges EV batteries quickly and effectively. Findings suggest the technique could increase power quality, system efficiency, and EV uptake.
Volume: 15
Issue: 1
Page: 238-247
Publish at: 2026-03-01
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