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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 44 Documents
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Search results for , issue "vol 15, no 3: september 2026" : 44 Documents clear
Sliding-mode assisted direct torque control for reliable wind turbine operation Nehal Ouassila; Dib Djalel; Billel Meghni; Dib Nour Elhouda
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1180-1189

Abstract

This paper investigates and compares the performance of two advanced control strategies, direct torque control (DTC) and sliding mode control (SMC), applied to a permanent magnet synchronous generator (PMSG) used in wind energy conversion systems. The control schemes aim to ensure efficient energy conversion and stable operation under variable wind conditions. The comparison is carried out through detailed simulations considering electromagnetic torque response, stator flux behavior, speed regulation, and robustness to disturbances and parameter variations. The results show that DTC provides a fast dynamic response with a relatively simple control structure, but suffers from torque and flux ripples and sensitivity to parameter variations. In contrast, SMC demonstrates higher robustness against uncertainties and disturbances, with smoother torque characteristics and improved speed regulation. Overall, the study indicates that SMC is a promising approach for enhancing the stability and performance of PMSG-based wind energy systems. Future work may explore hybrid strategies combining the fast response of DTC with the robustness of SMC, as well as intelligent control techniques to further optimize energy extraction.
A deep learning approach for electric vehicle battery charging duration estimation using IoT Tumuluri Kanthimathi; Adhimoolam Sairam; Durairaj Chandrakala; Moorthy Radhika; Bichagal Shadaksharappa; Pitchai John Britto; Minakshi Sanadhya; Balasubramanian Suganya; Chelliah Srinivasan
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1375-1385

Abstract

The rapid development of electric vehicles (EVs) has increased the desire for precise charging duration estimate to enhance charging station administration and elevate customer experience. This research presents a deep learning (DL) architecture that uses internet of things (IoT)-enabled data to categorise charging duration into short, medium, and long classifications. The assessment dataset comprises many numerical and categorical variables affecting battery performance and charging behaviour, providing a thorough foundation for prediction. The system utilises a deep neural network (DNN) architecture with nonlinear transformations and regularisation techniques, trained with adaptive optimisation to provide durable convergence. Extensive experiments indicate that the proposed model achieves an overall accuracy of 99.26%, markedly improving traditional machine learning (ML) techniques. These results highlight the potential of DL to adeptly discern complex linkages in charging dynamics, providing dependable predictions for duration classification. The framework provides an advanced basis for implementation in smart charging infrastructures, facilitating effective scheduling, minimizing waiting times, and endorsing predictive maintenance measures. It enhances the reliability and efficiency of EV charging ecosystems with data-driven, IoT-enabled DL technologies.
Performance evaluation of rooftop photovoltaic integration using self-consumption and self-sufficiency indicators with IoT-based monitoring Syukron Al-Fajri; Syafii Syafii
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1399-1408

Abstract

Indonesia’s energy transition has increased the importance of rooftop photovoltaic (PV) systems, yet their integration with the PLN grid remains challenging because intermittent generation affects supply-demand balance and complicates the evaluation of local PV utilization. This study proposes and evaluates a low-cost internet of things (IoT)-based AC-side monitoring system for residential rooftop PV integration using an ESP32 microcontroller and PZEM-004T energy sensors. The proposed framework monitors three measurement points, namely PV output, grid line, and household load, and classifies the operating condition into export and import states. Based on measured historical field data, the operational performance of the system is assessed using the self-consumption ratio (SCR) and self-sufficiency ratio (SSR). The results show that PV generation dominates during daytime operation, whereas household demand becomes increasingly dependent on the PLN grid from late afternoon to night. Across the observation period, daily SCR ranges from 1.08% to 6.86%, while daily SSR ranges from 3.07% to 74.61%, indicating limited overall self-consumption but relatively strong daytime load support. These findings show that the proposed low-cost AC-side IoT monitoring framework can effectively quantify rooftop PV-grid interaction and provide a practical basis for evaluating local PV utilization and grid dependence under actual household operating conditions.
Effect of power and voltage variations on transformer core losses and geometry for two distinct core materials Kamran Dawood; Furkan Gezer; Güven Kömürgöz Kırış; Semih Tursun
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp995-1008

Abstract

This study presents a comprehensive comparison of transformer core size and no-load losses, focusing on transformers with power ratings ranging from 400 kVA to 3200 kVA. The research evaluates transformer performance at three distinct primary voltage levels: 6 kV, 15 kV, and 33 kV, while maintaining a constant secondary voltage of 0.4 kV. Additionally, the study investigates the impact of two commonly used transformer core materials, M4 and H0, on core design, with a particular focus on their effects on no-load losses. Another central aspect of the analysis is the examination of core geometry, including the cross-sectional area and height of the transformer core, across various power ratings and voltage levels. The results reveal a clear relationship between core size and energy efficiency. M4 cores exhibit higher no-load losses compared to H0 cores; additionally, H0 cores demonstrate better overall efficiency, making them ideal for high-efficiency applications. The findings underscore the importance of selecting the right core materials and geometries to balance performance, efficiency, and size. These insights are intended to guide researchers and transformer designers in making informed decisions when developing energy-efficient solutions for diverse applications across the electrical power industry.
Sustainable e-mobility with controlled charging scheme based on grid energy using machine learning Archana Kadam; Ramesh Mali; Reena Gunjan; Virendra Shete; Pradeep Mane
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1036-1050

Abstract

The electric vehicle (EV) popularity has taken off among consumers, which has in turn led to efforts to create an efficient EV charging infrastructure. This paper addresses this challenge by proposing a scheduled charging scheme that uses real-time data from a grid-connected charging station at Baner, Pune, operated by Pune Mahanagar Parivahan Mahamandal Ltd (PMPML). The proposed system makes use of advanced machine learning techniques such as the Stochastic dual coordinate ascent (SDCA) and Fast Forest (FF) algorithm, both of which allow for precise and efficient computations to predict charging finish times and make optimal scheduling decisions. The use of these algorithms in conjunction with ToU tariffs is cost effective when compared to flat rate tariffs. Grid load analysis shows that scheduling according to time lowers peak demand, equalizes load distribution, and lowers operating costs. A quantitative comparison has demonstrated both grid stability and economic efficiency gains over uncontrolled charging. The result is an extremely flexible framework for different charging events or stations which will be a viable way of managing energy in the fast-growing EV charging networks.
Hybrid AC/DC and conventional AC house efficiency for net zero energy homes Taufik Taufik; Heru Nurwarsito; Tyler Bury; Rahman Azis Prasojo
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1458-1474

Abstract

The transition toward net-zero energy homes (NZEH) requires residential electrical systems that can efficiently integrate renewable generation, battery storage, and both AC and direct current (DC) loads. Although DC and hybrid AC/DC residential systems have been widely studied, limited work directly compares hybrid AC/DC and conventional AC house architectures under different grid standards, power levels, and AC/DC load ratios while considering DC bus losses. This study presents a MATLAB/Simulink-based steady-state efficiency comparison between hybrid AC/DC and conventional AC residential electrical systems. Twelve models were developed, consisting of six hybrid AC/DC and six conventional AC configurations under 120 V/60 Hz and 230 V/50 Hz standards. The models include PV generation, battery storage, inverter, AC/DC converter, multiple-input single-output (MISO) converter, and line-resistance effects. Results show that hybrid AC/DC houses achieve 4-11% higher efficiency than conventional AC houses when DC load demand remains below approximately 1.5-2.0 kW, mainly due to reduced conversion stages. At higher DC load levels, the efficiency advantage decreases because of copper losses in the 48 V DC bus. Increasing the DC bus voltage to 60 V reduces current-related losses and extends the efficient operating range. These findings indicate that hybrid AC/DC distribution is most suitable for residential applications with low-to-moderate DC demand, such as lighting, electronics, communication devices, and other DC-compatible appliances. The main contribution of this study is identifying the operating range, efficiency limit, and practical design implications of hybrid AC/DC residential distribution for future NZEH applications.
Performance evaluation of a GA-tuned PID controller for a buck-boost converter based on integral error metrics Mahabaleshwara Bhat P.; Subramanya Bhat
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1168-1179

Abstract

DC-DC buck-boost converters are widely used in photovoltaic (PV) systems to maintain a regulated output voltage under varying source and load conditions. Although genetic algorithms (GA) based proportional-integral-derivative (PID) controllers are commonly used, the influence of different integral error performance indices on controller behavior has not been systematically examined. This paper presents a detailed study of GA-based PID tuning for a buck-boost converter operating in both buck and boost modes. Four integral error metrics-integral absolute error (IAE), integral time absolute error (ITAE), integral square error (ISE), and integral time square error (ITSE)-are employed as fitness functions to obtain optimal controller gains. A detailed MATLAB/Simulink model of the converter working in continuous conduction mode (CCM) is developed to evaluate controller performance under step input, source transients, and load transients. The results demonstrate that the selected error metric significantly affects transient characteristics, including rise time, settling time, overshoot, and steady-state error. Controllers tuned using ITAE and ITSE provide better transient performance compared to IAE and ISE based tuning. The results highlight the critical role of objective function selection in GA based PID optimization and provide practical guidelines for buck-boost converters in PV applications.
Cost-effective hardware solutions for experimental validation in renewable energy emulation and storage systems Yassine El Asri; Abdellah Lassioui; Hassan El Fadil; Anwar Hasni; Marouane El Ancary; Hafsa Abbade; Mohammed Chiheb; Mohamed Koundi
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1287-1298

Abstract

Experimental validation is essential in renewable energy emulators and energy storage systems to ensure reliability, accuracy, and practical implementation. However, the high cost of AC/DC converters, measurement circuits, and microcontroller-based control platforms limits access to experimental validation, particularly in developing regions. This paper proposes cost-effective hardware and software solutions for validating renewable energy systems and storage management. The proposed approach is based on affordable power converters, low-cost voltage and current measurement circuits, data acquisition tools, and open-source control platforms. The methodology includes the design, implementation, and experimental testing of these low-cost solutions under different operating conditions. The obtained results demonstrate satisfactory measurement accuracy, stable behavior, and acceptable error margins, confirming the feasibility of the proposed setup for experimental validation. These solutions provide a scalable and accessible alternative to expensive laboratory platforms, enabling researchers and institutions with limited resources to perform reliable experimental studies and contribute to the advancement of sustainable energy technologies.
A novel MVVR controlled solar-PV fed MF-DVR for compensation of islanding and PQ issues in utility-grid integrated distribution system Tharinaematam Bhavani; Durgam Rajababu; Md Mujahid Irfan
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1023-1035

Abstract

The depletion of fossil fuels, planning of new industries, and increased population are considered significant motivations for the expansion of new power generation in line with the requisite load demand. In recent days, the solar-PV-based distribution generation is the most suitable power generation in a utility-grid-integrated distribution system. The main intention of this work is to present the effective DG scheme; it delivers the required active power during sudden interruptions, grid-islanding, and sudden block-outs. And also enhancing the voltage stability during voltage-harmonics, voltage sags/swells, and unsymmetrical fault conditions occurred in the utility-grid integrated distribution system through solar-PV fed multi-functional dynamic-voltage restorer (MF-DVR) device. The effective compensation performance of MF-DVR relies on viable reference voltage signals, which are produced by well-known control schemes reported in literature studies. But these regular schemes have reported that the major problems are highlighted and have been eliminated by proposing the novel modified voltage vector reference (MVVR) control scheme. The proposed MVVR controller perfectly produces the unique reference voltage signals for delivering a feasible switching pattern to the MF-DVR device. In this work, the design and performance of the proposed MVVR-controlled solar-PV-fed MF-DVR have been verified to enhance power quality and grid-islanding issues through MATLAB/Simulation software tool. The extracted simulation findings are presented with appealing interpretations complying with IEEE-519/2022 standards.
Near-zero NDZ islanding detection for multi-source DGs via hybrid ANFIS and adaptive fuzzy classification Madamaneri Ramya; Thangellamudi Devaraju
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1117-1131

Abstract

Increased penetration of distributed generation (DG) increases the likelihood of inadvertent islanding, in which traditional active/passive approaches are plagued with large non-detection zones (NDZ) and power-quality trade-offs. This article introduces a hybrid islanding detector that combines an adaptive neuro-fuzzy inference system (ANFIS) and an adaptive fuzzy classifier, concurrently benefiting from active frequency drift and rate-of-change-of-frequency (RoCoF) while consuming multi-signal features-RMS/THD of voltage and current, frequency, and active/reactive power sensed at the PCC. This architecture eliminates fixed-threshold brittleness and reduces the NDZ to zero without compromising power quality. Innovative aspects are i) a stacked, real-time sampling approach (Ts = 5 ms) that supplies per-signal ANFIS modules and a main decision ANFIS, ii) subtractive clustering for generating fuzzy rules data-driven, and iii) low iq perturbation to maintain unity power factor when querying doubtful NDZ examples. MATLAB/Simulink experimentation on a seven-case, seven-stage multi-source PV-interfaced microgrid (including power-matched NDZ) demonstrates fast, robust trips at disconnection with retention of IEEE-1547 voltage/frequency envelopes; the structure achieves minimum/ideal detection times of 0.04 s and reliably indicates islanding at ~0.4 s in matched and mismatched conditions, achieving normal breaker trip expectations (

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