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31,458 Article Results

Development of 15/33 level constant and variable DC source inverter for different loading conditions

10.11591/ijape.v15.i3.pp1051-1063
Vijayaraja Loganathan , Dhanasekar Ravikumar , Ganesh Kumar Srinivasan , Deepak Balachandran Kasthuri
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.
Volume: 15
Issue: 3
Page: 1051-1063
Publish at: 2026-09-01

Engineering autonomous and resilient power grids for a fully electrified future

10.11591/ijeecs.v43.i3.pp683-694
Tole Sutikno
The global transition toward deep electrification is fundamentally reshaping the role of power grids in modern society. Rapid integration of renewable energy resources, distributed energy systems, electric transportation, and intelligent loads is transforming conventional electricity networks into highly dynamic, decentralized, and cyber-physical infrastructures. In this evolving landscape, traditional power grids designed primarily for reliable electricity delivery are no longer sufficient to address increasing operational complexity, climate-related disruptions, cybersecurity threats, and the growing demand for system flexibility. This editorial argues that the future of electrical power systems depends on the convergence of autonomy and resilience, enabling power grids to anticipate disturbances, adapt to changing operating conditions, coordinate distributed energy resources, and recover rapidly from unexpected events. It discusses recent advances in artificial intelligence, self healing grid technologies, grid-forming power electronics, digital twins, edge intelligence, and cyber-physical security as key enablers of autonomous grid operation. The editorial further highlights emerging research challenges, including human–AI collaboration, AI-native power systems, multi-energy integration, and future regulatory frameworks. Ultimately, autonomous and resilient power grids are presented as the foundational infrastructure supporting a secure, sustainable, and fully electrified future while accelerating the global transition toward carbon-neutral energy systems.
Volume: 43
Issue: 3
Page: 683-694
Publish at: 2026-09-01

Advanced AI-driven battery state estimation using deep learning and ensemble models

10.11591/ijape.v15.i3.pp1072-1093
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
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.
Volume: 15
Issue: 3
Page: 1072-1093
Publish at: 2026-09-01

Voltage stability analysis of power transmission systems using multi-index framework of hybrid whale and particle swarm optimization technique

10.11591/ijape.v15.i3.pp1439-1457
Titus Terwase Akor , Theophilus Chukwudolue Madueme , Chibuike Peter Ohanu , Tole Sutikno
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.
Volume: 15
Issue: 3
Page: 1439-1457
Publish at: 2026-09-01

Artificial neural network-enhanced solid-state DC breaker for ultra-fast protection in LVDC microgrids

10.11591/ijeecs.v43.i3.pp709-717
Eswaraiah Giddalur , Askani Jaya Laxmi
Low voltage direct current (LVDC) microgrids are increasingly adopted due to their efficiency in integrating distributed energy resources (DERs) and DC loads without multiple conversion stages. However, the presence of high amplitude fault currents and the vulnerability of electronic devices pose significant challenges to reliable protection. Conventional electromechanical switches suffer from slow response times, making them unsuitable for modern LVDC systems. Solid-state circuit breakers (SSCBs), with their millisecond-level interruption capability, offer a promising alternative. This paper introduces an artificial neural network (ANN)-based protection strategy integrated with a bi-directional SSCB for DC bus fault mitigation. The ANN is designed to optimize fault detection and clearing time, achieving rapid isolation within 0.25 ms. A feedforward two-layer neural network with 20 hidden neurons and a sigmoid activation function is trained using multiple algorithms, including Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. Simulation results demonstrate superior performance compared to conventional differential protection, with the LM algorithm providing the most accurate and efficient fault clearing. The proposed ANN-SSCB approach enhances system reliability, equipment safety, and overall resilience of LVDC microgrids.
Volume: 43
Issue: 3
Page: 709-717
Publish at: 2026-09-01

Precision enhancement in drone position control based on optimized PID: A comparative study

10.11591/ijra.v15i3.pp529-543
Imam Barket Ghiloubi , Latifa Abdou
This paper presents a comparative study of quadrotor trajectory tracking performance using proportional integral derivative (PID) controllers optimized by two nature-inspired metaheuristic algorithms: the flower pollination algorithm (FPA) and particle swarm optimization (PSO). A nonlinear dynamic model of the quadrotor is first established, capturing both translational and rotational motions. A cascaded PID control architecture is then designed, where the outer loop regulates position and the inner loop controls attitude and altitude. The PID gains are tuned offline by minimizing an objective function combining the integral of time-weighted absolute error (ITAE) and an overshoot penalization term. The optimized controllers are evaluated through three reference scenarios: step response, circular trajectory, and lemniscate trajectory. Quantitative performance metrics, including RMSE, MAE, IAE, and ITAE, are used to assess tracking accuracy. Results show that the PID-FPA controller significantly outperforms PID-PSO in terms of overshoot reduction, faster settling time, smoother responses, and improved tracking precision, especially for complex trajectories. A robustness analysis is finally conducted by introducing a realistic synthetic wind disturbance modeled using sinusoidal components and stochastic turbulence. The FPA-tuned PID demonstrates strong robustness, maintaining accurate trajectory tracking under aerodynamic perturbations.
Volume: 15
Issue: 3
Page: 529-543
Publish at: 2026-09-01

Real-time selective harmonic elimination in multilevel inverters using embedded metaheuristic optimization on PYNQ-Z2

10.11591/ijpeds.v17.i3.pp1914-1925
Taha Ahmad Hussein , Dahaman Ishak
This paper offers a structured methodology for regulating optimal control switching angles in multilevel inverters to achieve selective harmonic elimination (SHE) using embedded optimization algorithms on the PYNQ‑Z2 FPGA operating base. The proposed approach employs metaheuristic techniques including particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimization (GWO), slime mould algorithm (SMA), and whale optimization algorithm (WOA) to generate candidate switching angles across a wide range of modulation indices. For each modulation index, the most effective solution towards harmonic reduction and waveform quality is selected and implemented on the FPGA controller, enabling reliable real‑time operation with very low delay. Experimental validation of a 31‑level single‑phase inverter confirms the effectiveness of the method in eliminating selected harmonics and refining the fundamental output. Combining comparative algorithmic selection with FPGA‑based control demonstrates a systematic and efficient strategy for advanced inverter systems. A MATLAB/Simulink model is constructed to represent the 31-level inverter to enhance the practical aspect and study the frequency spectrum, where many harmonics that contribute to obtaining THD within the IEEE standards are eliminated. Also, the different types of losses are calculated based on the governing mathematical rates.
Volume: 17
Issue: 3
Page: 1914-1925
Publish at: 2026-09-01

The role of online public relations and social influence on customer behavior at Vietnamese commercial banks

10.11591/ijaas.v15.i3.pp1015-1028
Thao Phuong Nguyen , Tuan Minh Nguyen , Hang Thi Thanh Hoang , Huong Thi Thu Nguyen
This study examines the effects of personalization (Per), social media (SM), and timeliness (Tim) on online public relations (OPR) and customer behavior (CB), as well as the mediating role of OPR and the moderating role of social influence (Soc). Data from 337 customers of Vietnamese joint-stock commercial banks were analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that Per and SM significantly enhance OPR, whereas Tim does not affect OPR but directly influences CB. The indirect effects of Per, SM, and Tim on CB through OPR are not significant, indicating that OPR does not mediate the relationships. Although OPR has a small positive effect on CB in the moderation model, Soc neither directly affects CB nor moderates the OPR–CB relationship. These findings suggest that OPR supports digital relationship management but does not automatically translate communication activities into behavioral responses. Banks should therefore prioritize personalized, credible, transparent, and secure communication.
Volume: 15
Issue: 3
Page: 1015-1028
Publish at: 2026-09-01

Integrating large language models for context-aware decision making in autonomous mobile robots

10.11591/ijra.v15i3.pp607-620
Vishnu Kumar Mishra , Megha Mishra , Talasila Ram Kumar , Yenna Geetha Reddy , Gundla Rajesh , Battula Phijik , Bandla Srinivasa Rao
The dynamic evolution of industrial automation has created an imperative need to transition from inflexible, rule-based systems to flexible, intelligent agents that can facilitate human-robot collaboration. The purpose of this research was to integrate large language models (LLMs) with autonomous mobile robots (AMRs) to improve context-aware decision-making. The inflexibility of traditional systems has often hindered performance in dynamic environments, as systems often rely on predefined algorithms and sensor configurations. To improve this, a modular framework was created, consisting of a central processing unit and an LLM API to interpret natural language and process environmental information. Quantitative results have been clearly specified in the abstract, which states that the success rate in resolving navigation exceptions by the proposed framework was 89% with a 5% localization error rate. Moreover, substantial savings were observed in token usage and computational resources. This study provided an imperative framework for smarter industrial automation, filling the gap between mechanical precision and artificial intelligence. The practical experimentation of an AMR model gives an outcome of a successful navigation exception resolution rate of 89% by means of the proposed framework, with an error rate of 5% localized to each exception. Additionally, significant reductions in the number of tokens used and the time taken to process tokens provide a scalable means for developing contextually-based, robust, autonomous mobile platforms’ decisions.
Volume: 15
Issue: 3
Page: 607-620
Publish at: 2026-09-01

Optimization of tensile strength and modulus elasticity from recycled mask polymer blends

10.11591/ijaas.v15.i3.pp894-901
Indah Widiastuti , Slamet Jatmiko , Budi Harjanto , Arif Bagus Panuntun
This research aims to analyze the effect of differences in the proportion of recycled polypropylene (r-PP), compatibilizer, and type of mask on the tensile strength and modulus of elasticity of recycled mask product materials. The research design used in this research uses the Taguchi orthogonal L4 experimental method, which uses a quantitative approach, namely by manipulating certain conditions and providing control over the test samples. This research focused on the effect of varying the proportion of r-PP composition, maleic anhydride polypropylene (MAPP) compatibilizer, as well as varying the type of mask waste used. In this research, the composition of r-PP used was 10% and 30%. Meanwhile, the MAPP composition used is 0% and 6%. In this study, tensile testing was carried out in accordance with the American Society for Testing and Materials (ASTM) D 638 standard type V in the form of a dogbone. The results of the research show that differences in the proportion of r-PP in the composition of the polymer mixture have an effect on the tensile strength and modulus of elasticity of the recycled mask product material.
Volume: 15
Issue: 3
Page: 894-901
Publish at: 2026-09-01

Design of a novel single-source 15-level inverter with self-balancing capacitors and sensorless PWM control

10.11591/ijpeds.v17.i3.pp1950-1961
Taoufiq El Ansari , Ayoub El Gadari , Youssef Ounejjar
Multilevel inverters (MLIs) are widely used in energy-conversion systems because they generate high-quality AC voltages with reduced harmonic distortion. However, existing MLIs often require numerous power devices, multiple DC sources, voltage sensors, or dedicated capacitor-balancing controllers, which increases both hardware cost and control complexity. This paper proposes a single-phase 15-level inverter derived from the Packed U-Cell structure. It employs a single DC source, three capacitors, and a reduced number of power switches. Based on the number of power switches, gate drivers, diodes, capacitors, DC sources, output levels, and total standing voltage per unit (TSVpu), the proposed topology achieves a lower cost function than the compared topologies reported in the literature. The proposed topology achieves a relatively low total standing voltage per unit (TSVpu) of 4.43. Sensorless open-loop SPWM offers inherent capacitor-voltage self-balancing, eliminating the need for voltage sensors or additional balancing loops. MATLAB/Simulink validation at 2 kHz and a modulation index of 1 covers steady-state operation, load transients, nonlinear loading, and DC-source voltage fluctuations. For a 50 Ω–20 mH load, the current THD is 2.20% without an output filter, confirming suitability for energy-conversion systems.
Volume: 17
Issue: 3
Page: 1950-1961
Publish at: 2026-09-01

A novel machine learning based maximum power point tracking in interleaved buck-boost converter for portable solar-powered electric vehicle charging in rural areas

10.11591/ijpeds.v17.i3.pp2247-2258
C. Niranjana , P. K. Vineeth Kumar , J. J. Jijesh , G. B. Arjun Kumar , Dileep Reddy Bolla , K. N. Sunil Kumar
Need for off-grid electric vehicle (EV) charging solutions, using intelligent control systems, such as machine learning (ML)-based maximum power point tracking (MPPT), to harness solar energy, offers a means of optimizing efficiency even in the face of fluctuations. This innovative strategy combines clean energy, cutting-edge power electronics, and practical application, which makes it perfect for fostering innovation in areas with inadequate infrastructure. For rural areas without grid infrastructure, this paper presents a novel design and performance assessment of a portable solar-powered EV charging system. To maximize solar energy harvesting and charging efficiency, the system combines an interleaved buck-boost converter with an ML-driven MPPT algorithm. It is appropriate for small electric vehicles (EVs) like auto rickshaws because it uses a 48 V lithium iron phosphate (LiFePO₄) battery. A supervised regression model trained on real-time electrical (voltage, current, and power) and environmental (temperature, irradiance) parameters is used to implement the MPPT algorithm. The system was created using MATLAB/Simulink, and the key performance parameters were evaluated using real-time information. Analyses of the key performance metrics like charging efficiency, converter stability, and tracking accuracy show a superior energy harvesting efficiency of 97%.
Volume: 17
Issue: 3
Page: 2247-2258
Publish at: 2026-09-01

Sliding-mode fuzzy logic controller based direct torque control and ripple minimization of induction motors

10.11591/ijpeds.v17.i3.pp1714-1727
S. Sujitha , Hima Bindu Eluri , P. B. Savitha , S. Venkateshwarlu
Conventional direct torque control is one of the most effective strategies for managing the torque of an induction machine (DTC), which is one of the most common types of machines. Instead, the DTC's inadequate control ability is made worse at low speeds by the audible flux and torque waves that are generated by its extremely low switching frequency. This is because the DTC's switching frequency is exceedingly low. In an effort to address these concerns, a variety of direct torque control strategies focused solely on flux and torque as their primary concerns. An improvement in DTC control and the elimination of ripples in induction motors are the goals of this research, which introduces a sliding mode fuzzy logic controller technique. The complexity of the algorithm, sensitivity of the parameters, the tracking speed, the switching loss, and the ripple reduction properties of the technique will all be thoroughly investigated. Simulation of the control mechanism is performed with MATLAB/Simulink in order to verify that it functions as intended.
Volume: 17
Issue: 3
Page: 1714-1727
Publish at: 2026-09-01

Classification of encryption attacks and strategies for mitigation

10.11591/ijict.v15i3.pp1238-1253
Anas Maaifi , Khalid Zine-Dine
Cryptography is essential for securing digital communications, yet it remains vulnerable to various malicious attacks. These attacks can be classified based on the type of cryptography they target symmetric or asymmetric. This paper presents a comprehensive classification of encryption attacks, examining the specific vulnerabilities associated with each cryptographic approach. By analyzing these attack vectors, the study shows the importance of understanding weaknesses in cryptographic systems. Furthermore, it proposes several mitigation strategies to strengthen defenses and enhance protection of sensitive information in the digital domain.
Volume: 15
Issue: 3
Page: 1238-1253
Publish at: 2026-09-01

Wavelet-based spectrum sensing with improved thresholding for enhanced detection in cognitive radio networks

10.11591/ijict.v15i3.pp1123-1134
Nur Hanis Abdul Rani , Mas Haslinda Mohamad , Nurusolihah Zamri , Nor Khairiah Ibrahim
Cognitive radio (CR) technology is an adaptive, intelligent radio and network technology that can automatically detect available channels in a wireless spectrum. Spectrum sensing is the most important component in CR due to its ability to sense and recognize parameters related to the radio channel characteristics. However, there are some spectrums that are not used known as spectrum holes. It is challenging to accurately identify these spectrum holes, especially when employing traditional energy detection techniques, which suffer from incorrect threshold selection at low signal-to noise ratio (SNR) levels. This work suggests a wavelet-based spectrum sensing technique in conjunction with an enhanced thresholding method to improve detection accuracy and decrease noise to overcome this constraint. MATLAB simulations are used for evaluating three threshold functions: hard, soft, and improved. The results indicate that the improved threshold achieves superior denoising performance and a higher detection probability compared to the traditional energy detection method. In this study, the energy detection technique was also implemented for comparison with the wavelet-based approach. The findings reveal that wavelet-based sensing consistently provides a higher detection probability (𝑃𝐷𝐸𝑇), demonstrating its effectiveness and reliability for cognitive radio (CR) application.
Volume: 15
Issue: 3
Page: 1123-1134
Publish at: 2026-09-01
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