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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 658 Documents
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Advanced AI-driven battery state estimation using deep learning and ensemble models N. Sumana Keerthi; B. Jyothi; M. Sharanya; CH. Srinivas; K. Shravani; V. Sanjeeva Rao; Teerdala Rakesh; Nittala Ramchandra; Malligunta Kiran Kumar; K. V. Govardhan Rao
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.pp1072-1093

Abstract

Accurate estimation of the state of charge (SOC) and state of health (SOH) of lithium-ion batteries is essential for improving the performance, safety, and lifespan of electric vehicles (EVs). Traditional estimation methods often face challenges such as high computational complexity, limited adaptability to battery aging, and reduced accuracy under varying operating conditions. To overcome these limitations, this paper presents a hybrid data-driven framework that combines machine learning and deep learning techniques for SOC and SOH prediction. For SOC estimation, linear regression, recurrent neural networks (RNN), gated recurrent unit (GRU), and stacked long short-term memory (LSTM) models are employed. For SOH prediction, ensemble learning methods, including stacking regressor, tree-based pipeline optimization tool (TPOT) regressor, and hybrid GRU-LSTM models, are utilized. The proposed models are evaluated using publicly available lithium-ion battery datasets under different charge-discharge conditions. Results show that deep learning approaches achieve superior performance, with GRU-LSTM and stacked LSTM models providing highly accurate SOC estimation (R² ≈ 0.993, RMSE ≈ 0.015), while the TPOT-based ensemble model delivers near-perfect SOH prediction (R² ≈ 1.0). A web-based implementation further enables real-time battery monitoring, demonstrating the framework’s practicality for advanced battery management systems (BMS) in EV applications.
Voltage stability analysis of power transmission systems using multi-index framework of hybrid whale and particle swarm optimization technique Titus Terwase Akor; Theophilus Chukwudolue Madueme; Chibuike Peter Ohanu; Tole Sutikno
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.pp1439-1457

Abstract

The persistent increase in grid collapse has necessitated the need for robust solutions for stability. Hybrid whale and particle swarm optimization (WAPSO) algorithm integrated with multi-index stability indices (MIS) has been applied to enhance voltage stability in this paper. The method is tested on the Nigeria 48-bus, 330 kV transmission system and simulated in MATPOWER embedded in MATLAB. The WAPSO algorithm is optimized with parameters w = 0.4, c₁ = 1.4 and c₂ = 1.5. The outcome achieved 98.84% reduction in mean MIS from 0.3959 to 0.0048 and a robustness index of 0.0051. This depicts low variability of 0.057 and 100% success rate across five dynamic scenarios explored such as gradual load growth with 98.79%, sudden spikes achieved 98.84%, cyclic fluctuations at 98.67%, renewable uncertainty at 98.74%, and N-1 contingencies at 98.84% improvement, respectively. Critical lines, 34-35 and 24-33 exhibit more than 99% MIS improvement, while weak lines, 21-22 and 24-40 achieved 85–87% enhancement, indicating areas for further reinforcement. The LSI and FVSI are reduced to 0.0048 and 0.1477 showing 56.79% improvement respectively as against the 38.9% MIS improvement obtained with the PSO-GA. This demonstrates WAPSO as a highly robust and adaptable technique offering a scalable solution for enhancing grid reliability under dynamic conditions.
Performance analysis of un-equal rotor and stator length switched reluctance motor Mohammed Moanes Ezzaldean Ali; Nadheer A. Shalash
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.pp1190-1199

Abstract

The performance of the SRM motor is influenced by several design parameters, including the lengths of the rotor stack and stator stack. Typically, the SRM is designed with equal rotor and stator lengths. However, there are some limited exceptions where the motor has a rotor that is longer or shorter than the stator; such a motor can achieve a reduction in one or more of the following: iron loss, copper loss, weight, inertia, and cost. To evaluate the performance of the SRM when the stator and rotor lengths are unequal, a 3D-model of the 6/4 SRM was developed using Ansys Motor-CAD software. In this work, two cases were investigated: the first case involved configurations where the stator was shorter than the rotor, and the second case involved configurations where the rotor was shorter than the stator. In both cases, other motor dimensions and variables were held constant. The simulation results show that certain performance indicators of the motor are not negatively affected, where they remain almost constant or even change positively, while other performance indicators are negatively changed, these changes are not significant when the differences between the stator and rotor lengths are within 10%. The cases of unequal length of stator and rotor can be considered as a new option that is manipulated to achieve the optimal design of the SRM, especially for low cost, low inertia, and high-speed applications.
Intelligent fault diagnosis and protection in DG-connected systems using resistive superconducting fault current limiter and ANN-based detection Lekshmi R. Chandran; Ilango Karuppasamy; Manjula G. Nair
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.pp1009-1022

Abstract

Ensuring reliable fault diagnosis and rapid recovery in distributed generator (DG)-connected distribution systems is critical, as the integration of DG sources significantly elevates fault current levels. This study proposes an integrated approach that combines a resistive superconducting fault current limiter (RSFCL) with an artificial neural network (ANN)-based intelligent fault diagnosis framework. The objective is to limit excessive fault currents while improving detection accuracy under varying network configurations. The RSFCL is strategically placed by analyzing fault current magnitude, voltage quality, and resistance value to achieve effective current limitation without compromising system stability. Meanwhile, the ANN employs symmetrical components of current and voltage as diagnostic features. To enhance robustness, correlated variables are identified and eliminated during feature selection, strengthening the model’s fault discrimination capability. Simulation results demonstrate that the optimal RSFCL placement reduces fault current contribution ratios by up to 82.16% under symmetrical fault conditions. The ANN-based fault detection model achieves a validation accuracy of 99.7%, outperforming conventional threshold-based methods by minimizing nuisance tripping and improving circuit breaker coordination. Overall, the combined RSFCL–ANN framework provides an effective and intelligent solution for fault diagnosis and protection in DG-integrated power systems.
Development of 15/33 level constant and variable DC source inverter for different loading conditions Vijayaraja Loganathan; Dhanasekar Ravikumar; Ganesh Kumar Srinivasan; Deepak Balachandran Kasthuri
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.pp1051-1063

Abstract

In this paper, a design of symmetric and asymmetric multilevel inverter (MLI) with few quantities of switch is presented. The structure can be able to operate with both symmetric and asymmetric sources. The presented model is capable of producing output levels of 15 with symmetric structure and 33 with asymmetric structure. The tendered circuit is constructed with 14 switches and 7 sources. The presented MLI can be placed in moderate-voltage applications such as: electrical machine drives. The circuit's switching sequences are framed by a detailed discussion from its operation. In MATLAB/Simulink, the inverter is simulated for resistive, resistive-inductive, and induction motor loads, and the results are portrayed. Also, the working of the inverter is monitored in terms of harmonics presence in the load signals. Additionally, the presented MLI is developed in real time to evaluate its performances. The results obtained from the real time inverter are found satisfactory.
Performance analysis of a photovoltaic system under real conditions using modeling and simulation Fatima Falil; Noureddine Benabadji; Ahmed Allali; Hamid Bouzeboudja
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.pp1409-1421

Abstract

The main objective of this study is to evaluate the performance of a photovoltaic (PV) system under real-world conditions by analyzing three key climatic variables on a monthly scale: insolation, average daily temperature, and photovoltaic power output. Insolation, defined as the average daily sunshine duration, is a critical parameter for estimating the solar energy potential in the Ghardaïa region, which is characterized by a desert climate. Average daily temperature directly influences the efficiency of PV panels, as elevated temperatures tend to reduce their performance. Photovoltaic power output, expressed in kWh/m²/day, is calculated based on solar radiation, providing a measure of actual energy generation under prevailing climate conditions. The analysis of these three climatic parameters offers valuable insights into seasonal variations and their impact on solar energy production in the Ghardaïa region. To optimize PV system performance, the study emphasizes the importance of accurate system design supported by advanced numerical methods. We extend the modeling from individual solar cells to complete PV modules and arrays, accurately reproducing their electrical characteristics. Simulations are performed in MATLAB/Simulink to validate the proposed model, which is based on an enhanced two-diode representation that reflects realistic operating conditions.
Design development and techno-economic assessment of a solar-powered three-row chickpea leaf nipping and collecting machine for sustainable farming Prashant Kadi; Basanagouda Ronad
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.pp1351-1365

Abstract

Agricultural mechanization enhances productivity and reduces manual labour in labour-intensive regions. Chickpea, a major pulse crop in India, is widely cultivated in semi-arid regions of North Karnataka, particularly in Vijayapura district. In 2024, 500,000 hectares of pigeonpea and 1.9 million hectares of chickpea were sown in this region. In chickpea, leaf nipping is a vital agronomic practice that encourages branching and improves yields. However, this practice is done manually, making it highly labour-intensive, time-consuming, and less feasible for small-scale farmers. To address these challenges, the current research focuses on the design, development, and techno-economic evaluation of a solar-powered, three-row leaf-nipping and collecting machine. This machine is equipped with a 150 W solar PV system, six 15 W BLDC motors, and an adjustable cutter-head assembly for efficient operation. A techno-economic analysis was conducted to evaluate cutting force, torque, power consumption, and battery discharge characteristics, as well as the payback period, cost savings, and improvements in farm income. Field trials conducted on a 9-acre farm in Vijayapura, receiving an average annual solar irradiation of approximately 1950 kWh/m², demonstrated 350-400 kg of leaf collection and a 25-30% increase in crop productivity. The machine provides a cost-effective, eco-friendly, and scalable solution for small and medium-scale farmers.
Systematic lamp replacement for energy efficiency improvement: a comparative luminous-efficacy analysis S. Jayachandra; Shaik Rafi Kiran
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.pp1275-1286

Abstract

Achieving energy efficiency and establishing energy conservation for driving future energy is one of the prime concerns of any nation. Besides, the lighting system, one of the major contributors of energy consumption set ahead in fixing the aforesaid concerns to the extent possible. When looking at environmental benefits and energy saving opportunities, among several available luminaries, LEDs stand far superior to standard tubular fluorescent lamps (TFL). However, the power consumption pattern differs by its manufacturing constraints which are notified to the consumers through the star ratings and energy efficiency labels. Energy savings can be achieved by systematically replacing the conventional TFLs by means of energy efficient LED lamps (EEL). This paper focuses on the practicality of use concerning the scheme of lamp replacement to achieve better energy efficiency. To carry out these isometrics, Bureau of Energy Efficiency (BEE) approved TFL and LED lamps of different brands and star ratings were used. In line with methodology discussed in the manuscript, it is obvious that by replacing 36 W and 28 W standard TFLs through appropriate EELs, the respective annual energy savings of about 11-16 kWh and 7 kWh can be achieved. The results highlight that replacing the lamps randomly can be futile; however, a strategic process in the selection and proximate replacement ensures an optimal level of efficiency. This leads to an increased lumen output, longer lamp life, or both, which ultimately saves an appreciable amount of energy. The prospective research may focus on inter EEL replacement isometrics to achieve microtic energy efficiency.
Design of an iterative AI enhanced STATCOM-controlled hybrid renewable energy system with multi-agent coordination and predictive stability intelligence sets Bhishan Wadhai; Nitin Dhote; Mohan Lal Kolhe
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.pp1147-1156

Abstract

Renewable energy integration causes intermittency, nonlinear dynamics, and grid-code restrictions in modern power systems. Although hybrid renewable energy systems combining wind, photovoltaic, and fuel cell sources increase energy availability, conventional control approaches often fail to maintain voltage stability, power quality, and rapid fault recovery under varying operating conditions. High renewable penetration and noisy conditions worsen these concerns. Existing methods typically address voltage regulation, transient stability, fault resilience, and power sharing independently using fixed or offline-tuned controllers, limiting adaptability during grid disturbances. To overcome these challenges, this study proposes an AI-enhanced STATCOM-controlled hybrid renewable energy system with learning-based control, predictive stability assessment, and multi-agent coordination. Adaptive reactive power support, noise-resilient fault detection, renewable source power sharing, predictive voltage regulation, and physiologically inspired transient stability prediction using hierarchical reinforcement learning. The simulation results maintain system voltage deviation within ±2%, harmonic distortion below 2%, fault detection within 7 ms, and transient stability prediction accuracy above 98% across varied operating conditions. Voltage recovery, overshoot suppression, and resource utilization efficiency improve above benchmark techniques. Thus, findings demonstrate that AI- enhanced STATCOM works as cognitive grid-interfacing agents rather than passive compensators for improving stability, power quality, and operational resilience in various deployment settings.
Predictive maintenance for induction motors: a novel synergy of deep learning and machine learning techniques V. Rajini; Karunya Harikrishnan; Krismadinata Krismadinata
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.pp1157-1167

Abstract

Condition monitoring of induction motors is vital for preventing unexpected downtimes and minimizing the maintenance costs in industrial settings. A predictive maintenance model for early detection of faults is proposed. The motor current, flux, vibration, thermal and acoustic emission signatures are commonly used for fault detection as these signals reveal fault specific frequency components. Signal processing techniques like wavelet transform and Hilbert transform are used to identify faults at incipient stages. Machine learning and deep learning models are used nowadays to extract features and classify the faults accurately. The current and flux signals from healthy and faulty motors for inter turn faults were analyzed in this work and features were extracted through various signal processing methods. These features were then used to train models, including deep learning architectures, to classify motor faults. While the classical machine learning models provided a reasonably accurate fault classification, the convolutional neural network provided a very good classification accuracy. The findings show that deep learning models excel in detecting faults, especially under noisy and varying operational conditions, outperforming the traditional methods. These models offer a scalable, real-time solution for improving the reliability and efficiency of induction motor and facilitate more reliable assessments and contribute towards energy efficiency and extended lifetime of equipment.

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