cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Applied Power Engineering (IJAPE)
Published by Institute of Advanced Engineering and Science
ISSN : 22528792     EISSN : 27222624     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
International Journal of Applied Power Engineering (IJAPE) focuses on the applied works in the areas of power generation, transmission and distribution, sustainable energy, applications of power control in large power systems, etc. The main objective of IJAPE is to bring out the latest practices in research in the above mentioned areas for efficient and cost effective operations of power systems. The journal covers, but not limited to, the following scope: electric power generation, transmission and distribution, energy conversion, electrical machinery, sustainable energy, insulation, solar energy, high-power semiconductors, power quality, power economic, FACTS, renewable energy, electromagnetic compatibility, electrical engineering materials, high voltage insulation technologies, high voltage apparatuses, lightning, protection system, power system analysis, SCADA, and electrical measurements.
Arjuna Subject : -
Articles 40 Documents
 1   2   3   4 
Search results for , issue "Vol 15, No 1: March 2026" : 40 Documents clear
Robust SOC estimation for lithium-ion batteries under faulty charging scenarios using sliding mode observer techniques Mahiddine, Soulef; Djeddi, Abdelghani; Djalel, Dib
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp46-58

Abstract

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.
A technical review of implemented pulsed electric field generators with different topologies Subramani, Krishnaveni; Mary, S. Jeroline
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp412-420

Abstract

Pulsed electric field technology (PEF) seeks application in a variety of industries, such as food processing, wastewater treatment, and biomedical engineering, as it provides a non-thermal substitute for conventional thermal pasteurization techniques. The PEF generators are an increasingly important component of this technology since it necessitates high voltage in the range of 2 kV/cm to 100 kV/cm in food processing to inactivate the microorganisms. Different PEF profiles are required based on different foods and the type of microorganisms present in it. The size of existing PEF producers and space limitations are the major challenges in this technology. Hence, there is a growing need to develop laboratory-scale PEF generators to study and analyze the PEF electrical profile for the specified applications. While the single MOSFET PEF generator is appropriate for high frequency applications, the series linked MOSFET PEF generator, one of the PEFs produced in our lab, is found economical. The voltage boosting concept is used to develop 1.62 kV pulses at 52 kHz from 120 V DC input. This paper majorly studies the circuit topologies, switching strategies, and output performances of PEF generators implemented in the laboratory.
Artificial intelligence for optimizing renewable energy systems: techniques, applications, and future directions Benitez, Ian B.; Cuizon, Edwin C.; Dizon, Jose Carlo R.; Badec, Kristina P.; Varela, Daryl Anne B.
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp275-288

Abstract

The integration of artificial intelligence (AI) is critically transforming the renewable energy sector. This review synthesizes AI's role in optimizing solar and wind energy systems, focusing on power forecasting, system optimization, and predictive maintenance. The research goal was to systematically analyze how diverse AI techniques enhance these critical aspects. Key findings indicate AI's capacity to substantially improve short-term solar irradiance and wind power forecasts (e.g., via SARIMAX, long short-term memory (LSTM), and hybrid deep learning models), dynamically manage energy flow in smart grids and microgrids, optimize maximum power point tracking (MPPT) in photovoltaic (PV) systems, and enable proactive maintenance through anomaly detection in wind turbines using IoT-integrated AI. Key conclusions reveal that AI significantly enhances the efficiency, reliability, and economic viability of solar photovoltaic and wind power generation, offering superior adaptability and predictive capabilities over traditional methods. While AI is important for the global transition to cleaner energy, persistent challenges related to data quality and availability, model interpretability, and cybersecurity must be addressed to fully unlock its potential in practical renewable energy applications.
Induction motor simultaneous fault diagnosis based on Takagi-Sugeno models Souri, Samira; Louazene, Mohamed Lakhdar; Djeddi, Abdelghani; Soufi, Youcef
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp195-210

Abstract

This article proposes a model-based approach to the concurrent diagnosis of stator and rotor faults in induction motors (IMs) using Takagi-Sugeno (TS) fuzzy models. Fault-free detection is essential to prevent unexpected downtime and economic loss in industrial applications. The study first develops a dynamic model of the IM in the synchronized reference frame with the rotor under healthy and faulty operations. Different fault conditions like stator inter-turn short circuits, defective rotor bars, and combination thereof are considered. A TS model for every case is developed based on the precise nonlinear model. Simulation outcomes prove the validity of the new models in simulating the dynamic response of the motor under faulty operating modes. The residual signals are used to compare the performance of the model in fault isolation. The proposed method offers a classification that inherently separates between fault types. Such a contribution presents an efficient real-time fault detection and predictive maintenance facility, which renders it suitable for hardware-in-the-loop application in intelligent drive systems.
High impedance fault discrimination in microgrid power system using stacking ensemble approach Vinayagam, Arangarajan; Mohandas, Raman; Chindamani, Meyyappan; Sujatha, Bhadravathi Gavirangapa; Mishra, Soumya; Sundaramurthy, Arivoli
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp98-109

Abstract

High impedance (HI) faults in microgrid (MG) power systems are non-linear, intermittent, and have low fault current magnitudes, making them challenging to detect by typical protective systems. Consequently, it is imperative to implement a sophisticated protection system that is dependent on the precision of fault detection. In this study, a stacking ensemble classifier (SEC) is proposed to discriminate HI fault from other transients within a photovoltaic (PV) generated MG power system. The MG model is simulated with the introduction of faults and transients. The features of data set from event signals are generated using the discrete wavelet transform (DWT) technique. The dataset is used to train the individual classifiers (Naïve Bayes (NB), decision tree J48 (DTJ), and K-nearest neighbors (KNN)) at initial and meta learner in the final stage of SEC. The SEC outperforms other classification methods with respect to accuracy of classification, rate of success in detecting HI fault, and performance measures. The outcomes of the classification study conducted under standard test conditions (STC) of solar PV and the noisy environment of event signals clearly demonstrate that the SEC is more dependable and performs better than the individual base classification approaches.
Optimal design of three-phase solar PV integrated unified power quality conditioner (UPQC) Pawar, Yogesh S.; Kadu, Mahesh; Tapre, Pawan C.; Wankhede, Dinesh S.; Rewatkar, Rajendra M.; Choudhary, Swapna M.; Shriwastava, Rakesh G.
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp1-13

Abstract

This research investigates the utilization of a unified power quality conditioner (UPQC) to address power quality issues in the electrical grid and mitigate harmonics introduced by non-linear loads. The UPQC system is augmented by a combination of photovoltaic (PV) and battery energy storage system (BESS). Typically, the PV system supplies active power to the load. However, in cases where the PV system cannot provide sufficient power, the BESS is activated to ensure a continuous power supply, particularly during prolonged voltage interruptions. To enhance system reliability and reduce dependency on environmental conditions, a hybrid PV-BESS system is proposed. The inclusion of the BESS improves long-term voltage support capabilities, simplifies the DC-link voltage regulation algorithm, and facilitates the production of clean energy. For efficient phase synchronization operation of the UPQC controller under unbalanced and distorted grid voltage conditions, a self-tuning filter (STF) integrated with the unit vector generator (UVG) technique is employed.
Sensorless control strategy for brushless doubly fed reluctance generator under voltage flickering at point of common coupling Paul, Manish; Parida, Adikanda; Das, Anu Kumar
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp383-392

Abstract

The brushless doubly-fed reluctance generator (BDFRG) is widely used in grid-connected wind energy conversion systems (WECS). It has been observed that there is a continuous voltage flickering at the point of common coupling (PCC) between the BDFRG power terminals and the alternating current (AC) microgrids due to either the load variations or wind turbine output variations. Under such circumstances, sensorless control of BDFRG using the existing model reference adaptive system (MRAS) models exhibits erroneous active power output. This is because the variables selected in these models are directly or indirectly dependent on the voltage at the PCC. In this paper, a sensorless control mechanism for the BDFRG is proposed, which provides better performance in terms of control accuracy. Moreover, the planned scheme is insensitive to the parameter variations of the BDFRG. The performance of the planned system has been tested with a voltage flickering of 50% for 1 ms at the PCC. The stability test presented in this paper reveals that the model is robust and error-free against the noise disturbances. The planned system is implemented using proper simulations and a hardware platform with a practical BDFRG of 2.5 kW, and a dSPACE CP1104 module.
Machine learning-driven prognostics for lithium-ion batteries: enhancing RUL prediction and performance in smart energy storage systems Rajanna, Bodapati Venkata; Seenu, Aaluri; Krishnaiah, Kondragunta Rama; Peddinti, Anantha Sravanthi; Prakash, Nelaturi Nanda; Seshukumari, Bandreddi Venkata; Ambati, Giriprasad; Ahammad, Shaik Hasane; Kumar, Chakrapani Srivardhan; Rao, Allamraju Shubhangi
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp257-274

Abstract

In the evolving landscape of energy systems, batteries play a critical role in enabling hybrid and stand-alone renewable energy storage solutions. Precisely estimating battery life and remaining useful operational life will go a long way in enhancing the efficiency of the system with assured reliability in smart power storage devices. This report comprehensively surveys advanced approaches in the management of batteries through state-of-the-art artificial intelligence tools-support vector machines, relevance vector machines (RVM), long short-term memory (LSTM) models, and bayesian filters-that are being used with a view to enhancing remaining useful life (RUL) estimates and making real-time system health monitoring capabilities possible. Modeling approaches surveyed include state estimation, capacity, and thermal management, while discussing their applicability to lithium-ion batteries. The review also explores publicly available battery datasets, feature engineering strategies, and hybrid diagnostic frameworks. A technoeconomic perspective is provided to assess system performance in renewable-integrated power grids. This paper aims to consolidate current knowledge, provide comparative insights into the strengths and limitations of different approaches, and highlight open research challenges to guide future developments in smart AI-enabled battery systems that support sustainable and resilient energy infrastructure.
A novel analytical hybrid optimization methodology for maximizing renewable energy integration in radial distribution networks Ouali, Fateh; Lahaçani, Narimen Aouzellag; Hamoudi, Yanis
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp153-164

Abstract

Integrating distributed generation (DG) units into radial distribution systems (RDS) presents significant challenges, including voltage instability, power losses, and compliance with modern grid standards. To address these limitations, this study proposes a novel hybrid optimization methodology that combines advanced mathematical models with iterative power flow analysis. The approach introduces a multi-objective optimization framework that integrates voltage sensitivity factors, power loss indices, and voltage stability measures. A key innovation is the use of voltage stability indices (VSIs) as dynamic weighting factors to guide the optimization process, ensuring a balanced trade-off between minimizing power losses and enhancing network stability. This framework provides a precise and scalable solution for optimizing DG placement and sizing simultaneously. The methodology is validated on the IEEE 33-bus distribution system, demonstrating a 68% reduction in power losses, a 4.88% improvement in voltage stability, and a 70.4% DG integration rate, all achieved without altering the network configuration. These results highlight the proposed framework’s potential to enhance the resilience, efficiency, and reliability of RDS, offering a robust and standards-compliant solution for DG integration.
Hybrid system energy simulation for housing Junaidi, Agus; Rahmaniar, Rahmaniar; Suwarno, Suwarno; Cahyadi, Catra Indra; Pangaribuan, Wanapri; Hutabarat, Hot Marindo; Tambunan, Arsita Devi; Panjaitan, Albert
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp59-68

Abstract

Fossil fuel power plants are still used as a source of energy around the world and have a significant impact on emissions and environmental pollution. To reduce these emissions, renewable energy offers a solution that can be applied in future housing. This study proposes a simulation using HOMER to determine the most cost-effective composition of hybrid renewable energy systems in housing. This simulation can combine photovoltaic (PV) systems, wind power (WP), and a converter that functions to change DC to AC from PV to obtain an alternating current (AC) system. The hybrid combination of PV and WP proves to be the most appropriate and economical choice at the research location. The results of the study showed that the installation of a hybrid system in housing, with an initial investment cost of IDR 107,474.43 million and an annual operating cost of IDR 22,540.23 million, is 41% lower than conventional fossil fuel-based systems. Research data shows that the project's payback period is estimated to be around 11 years. These findings can be recommendations for similar systems in regions with similar contours and geography. Apart from that, positive monetary impacts can provide incentives for policymakers to implement similar hybrid systems, thereby contributing to the goal of global emissions neutralization.

Page 2 of 4 | Total Record : 40

 1   2   3   4 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol 15, No 1: March 2026 Vol 14, No 4: December 2025 Vol 14, No 3: September 2025 Vol 14, No 2: June 2025 Vol 14, No 1: March 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 3: December 2020 Vol 9, No 2: August 2020 Vol 9, No 1: April 2020 Vol 8, No 3: December 2019 Vol 8, No 2: August 2019 Vol 8, No 1: April 2019 Vol 7, No 3: December 2018 Vol 7, No 2: August 2018 Vol 7, No 1: April 2018 Vol 6, No 3: December 2017 Vol 6, No 2: August 2017 Vol 6, No 1: April 2017 Vol 5, No 3: December 2016 Vol 5, No 2: August 2016 Vol 5, No 1: April 2016 Vol 4, No 3: December 2015 Vol 4, No 2: August 2015 Vol 4, No 1: April 2015 Vol 3, No 3: December 2014 Vol 3, No 2: August 2014 Vol 3, No 1: April 2014 Vol 2, No 3: December 2013 Vol 2, No 2: August 2013 Vol 2, No 1: April 2013 Vol 1, No 3: December 2012 Vol 1, No 2: August 2012 Vol 1, No 1: April 2012 Vol 10 More Issue