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30,376 Article Results

Optimal placement and sizing of DG and DSTATCOM in order to mitigate power losses in electrical distribution system

10.11591/ijape.v14.i4.pp826-841
Smrutirekha Mahanta , Manoj Kumar Maharana
The emphasis is now shifting away from conventional methods of power generation and towards unconventional distributed energy resources (DERs) located at distribution voltage level due to the rapid depletion of fossil fuel supplies and significant environmental pollution. Emphasis on research into the applications of DERs found scope in microgrids and active distribution networks. The placement of DERs close to load centers aids with providing clean, reliable power to additional customers, reduce electricity losses along transmission and distribution lines and in event of faults it allows to operate in islanded mode. This manuscript focuses on power smoothing, which implies reduction of power loss, improved voltage levels, and voltage stability. This study aims to optimize the capacities and placements of distributed generations (DGs) and distribution static compensators (DSTATCOMs) in order to reduce real power loss and improve the voltage profile. The problem of voltage from undistributed energy resources can best be solved by DSTATCOM. The goal function of the direct load flow technique, which also makes use of voltage deviation and the loss sensitivity factor, is used in this study to pinpoint the ideal placement for the DG and DSTATCOM on the MATLAB platform. The method is tested using the 33 and 69 bus routes. When the results are compared to recent methodologies, they show encouraging results.
Volume: 14
Issue: 4
Page: 826-841
Publish at: 2025-12-01

Power smoothing in electrical distribution system using covariance matrix adaptation evolution strategy of aquila optimization

10.11591/ijape.v14.i4.pp842-858
Smrutirekha Mahanta , Manoj Kumar Maharana
This study introduces a novel hybrid optimization approach covariance matrix adaptation evolution strategy of aquila optimization (CMAESAO) to enhance power smoothing and minimize power losses in electrical distribution systems through the optimal allocation of D-STATCOMs. The method is tested on standard 33-bus and 69-bus systems. The CMAESAO algorithm efficiently identifies optimal locations and sizes of D-STATCOMs to achieve system performance improvements under constant power (CP), constant current (CC), and constant impedance (CI) load models. The results show that, for the 69-bus system, installing two D-STATCOMs yields optimal performance, reducing real power loss from the base value to 149.6368 kW, while three D-STATCOMs yield a slightly better voltage profile and VSI but only marginal additional power loss reduction (147.8951 kW), making two units more cost-effective. For the 33-bus system, three D-STATCOMs provide the best improvement in power quality and loss minimization. Voltage and current profiles confirmed improvement in voltage stability and reduced branch currents with optimized placements. Compared to other optimization techniques, CMAESAO demonstrates faster convergence and superior accuracy in minimizing losses, establishing its effectiveness for such multi-objective optimization problems. The study's novelty lies in integrating CMA-ES with aquila optimization to combine strong global search with adaptive exploration, resulting in robust and efficient power system enhancement. The proposed methodology contributes to smarter, more reliable distribution systems, supporting grid resilience and energy efficiency.
Volume: 14
Issue: 4
Page: 842-858
Publish at: 2025-12-01

Advancements in latent fingerprint recognition: a comprehensive review of techniques and applications

10.11591/ijai.v14.i6.pp4739-4748
Nandita Manchanda , Sanjay Singla , Gopal Rathinam
The identification of individuals has been in greater demand, whether it’s for criminal investigation, law enforcement, or the basic attendance marking system. Fingerprints are one of the most reliable and dependable methods for biometric identification systems; as such, they are crafted in the womb. Latent fingerprints refer to inadvertent impressions that are left behind at crime scenes and are of utmost importance in the field of forensic investigation and verification of the authenticity of an individual. However, because these impressions are unintentional, the quality of the prints uplifted is often poorer. To enhance the overall accuracy of fingerprint recognition, it is required to develop approaches that enhance the accuracy and reliability of existing techniques. Therefore, this paper provides a detailed analysis of the existing techniques for the reconstruction, enhancement, and matching of latent fingerprints.
Volume: 14
Issue: 6
Page: 4739-4748
Publish at: 2025-12-01

Enhancing academic conferences with AI: defining the role of the human AI editor

10.11591/ijai.v14.i6.pp4484-4493
Esteban Galan-Cubillo , Emilio Saez-Soro
Academic conferences serve as key platforms for knowledge exchange, yet they face challenges in managing large volumes of content efficiently while maintaining academic rigor. To address these challenges, this study introduces and evaluates the "AI editor": a novel human expert role who, using tools like ChatGPT, supervises, refines, and structures artificial intelligence (AI)-generated content in real time. Through a mixed-methods approach, we examine the role of AI in enhancing content creation and engagement. This approach included the experimental deployment of the AI editor in three sustainability-focused European academic conferences (in Spain and UK) and formative workshops with 127 university students from the same countries. While AI-assisted tools improve efficiency, concerns persist regarding traceability, reliability, and ethical oversight. Our findings indicate that AI by itself cannot guarantee scholarly integrity; continuous human oversight is indispensable. The AI editor ensures coherence, quality control, and compliance with academic standards, addressing a critical gap in AI adoption within research environments. This study contributes to the discourse on responsible AI use in academia by proposing a structured framework for its integration into conferences, balancing automation with human oversight. Moreover, it highlights the growing need for digital intelligence that enables researchers to interact ethically and effectively with AI and other digital technologies, fostering responsible and informed academic innovation.
Volume: 14
Issue: 6
Page: 4484-4493
Publish at: 2025-12-01

Design of a half-bridge inverter with digital SPWM control for pure sine wave output

10.11591/ijape.v14.i4.pp803-815
Jalil Akaaboune , Bouazza El Mourabit , Mohamed Oulaaross , Mohamed Benchagra
To foster the widespread adoption of solar power, especially that produced by photovoltaic (PV) systems, we must move beyond the mere utilization of renewable energy sources. Prioritizing cost-effective approaches through innovative grid integration is essential. This strategic transformation significantly contributes to the global expansion of electrical energy production. One pioneering approach involves the implementation of inverters operating at high frequencies to efficiently filter and eliminate undesirable current harmonics, thus enhancing system performance. This innovative technique relies on the generation of rapid complementary digital pulse width modulation (PWM) signals, complete with built-in dead time, to manage a half-bridge inverter with a single phase. The paper recommends employing the IR2110 driver, an often-used component for MOSFET switch management, to execute this strategy. The entire system is controlled by high-frequency PWM signals, meticulously programmed for precision, generated by a microcontroller driver board. With its adaptability to various renewable energy conversion devices, this methodology extends its utility beyond solar energy. Practical tests have confirmed the efficacy of this strategy. Future research in this field should scrutinize the effect of PWM on system stability and harmonic distortion, explore advanced modulation methods, align PWM approaches with upcoming power electronics technologies, and work towards improving system efficiency.
Volume: 14
Issue: 4
Page: 803-815
Publish at: 2025-12-01

Frequency response-based optimization of PID controllers for enhanced fluid control system performance

10.11591/ijape.v14.i4.pp1058-1070
Herri Trisna Frianto , Syahrul Humaidi , Kerista Tarigan , Dadan Ramdan , Doli Bonardo
Temperature and viscosity variations are known to affect the performance of proportional-integral-derivative (PID) controllers in fluid systems. However, there exist gaps in research relative to the thermal effects on the performance of PID based fluid systems. PID controllers are also utilized for fluid control to maintain stability and improve performance. This study aims to explore the influence of temperature and viscosity variations through frequency response analysis for the first time in this regard. Utilizing a controlled experimental setup, gain and phase values were measured across different temperature points. Bode and Nyquist plots were generated to observe system behavior, stability, and response to changes in temperature and fluid viscosity. The results show a clear inverse relationship between temperature and gain, with a notable phase lag increase as temperature rises. At 25 °C, the gain was measured at 15.83 dB with a phase of -52.63°, which gradually reduced to a gain of 13 dB and a phase of -61.53° at 80 °C. The Nyquist analysis revealed stable operation within this temperature range, but the shift in response indicates increased system vulnerability as viscosity decreases with rising temperature. The derived linear equations effectively model the gain-phase relationship, with an R² of 0.9985, suggesting a highly accurate fit. Overall, the study concludes that temperature-induced viscosity changes significantly impact PID-controlled fluid systems, emphasizing the need for adaptive control strategies in fluctuating environments.
Volume: 14
Issue: 4
Page: 1058-1070
Publish at: 2025-12-01

A hybrid framework of IoT and machine learning for predictive analytics of a DC motor

10.11591/ijape.v14.i4.pp870-878
Lalitha Kandasamy , Annapoorani Ganesan , M. Shunmugathammal
Many industrial applications utilize direct current (DC) motor as an essential element. It functions as the backbone of several industries and global pillar of manufacturing applications. The predictive analytics of motor is primary for preventing unpredicted downtime, reducing protection costs, and improving system effectiveness. This paper presents a hybrid framework integrating the internet of things (IoT) and machine learning (ML) for real-time predictive analytics of DC motors. The leveraging of machine learning algorithms in predictive maintenance of DC motors has shown significant potential in reducing downtime and increasing the lifespan of the motor. Therefore, a system for predictive analytics with machine learning strategy is proposed and message queuing telemetry transport (MQTT messaging) is included for effective information transmission between sensors and gateways. The data received from the sensors is utilized to make prediction about the remaining useful life of the motor and generate alerts for maintenance before failures occur. So, the integration of machine learning algorithms in predictive maintenance of DC motors is a promising approach to increase the reliability and efficiency of DC motors. The highest performance is achieved in random forest with accuracy of 93.4%.
Volume: 14
Issue: 4
Page: 870-878
Publish at: 2025-12-01

Backstepping control in speed loop combined with load torque observer-ESO for IPMSM in electric vehicle

10.11591/ijpeds.v16.i4.pp2271-2279
An Thi Hoai Thu Anh , Tran Hung Cuong , Nguyen Van Hoa
Electric vehicles are gaining popularity due to their environmental friendliness and the need to conserve dwindling fossil fuel resources. In this field, interior permanent magnet (IPM) motors are considered the top choice for propulsion systems due to their high efficiency, high torque-to-current ratio, durability, and low noise. To optimize the speed control performance of IPM motors in the presence of disturbances, a nonlinear speed control algorithm for IPM systems using the backstepping method is developed in this paper. Additionally, a load torque observer using the extended state observer (ESO) method is implemented to enable the system to respond quickly and accurately to load changes while minimizing the effects of disturbances, thereby enhancing the operation and reliability of electric vehicles. The simulation results, conducted in MATLAB/Simulink, demonstrate that the combination of backstepping control and ESO offers good stability for the motor system, while mitigating the impact of disturbances and load variations. This is an important step in optimizing the control system of electric vehicles, contributing to the improvement of performance and reliability in electric vehicle applications.
Volume: 16
Issue: 4
Page: 2271-2279
Publish at: 2025-12-01

Transmission line fault detection using empirical mode decomposition in presence of wind intermittency

10.11591/ijape.v14.i4.pp960-969
Venkata Krishna Bokka , E. R. Biju , Sai Veerraju Mortha , Majahar Hussain Mahammad , Shaik Mohammad Irshad
The regular fault detection approaches are failed to detect the faults in wind integrated transmission networks due to intermittency nature of the wind energy. More reliable schemes are required to accomplish the detection of faults in presence wind. This article proposed empirical mode decomposition (EMD) based fault detection scheme to detect various faults in wind integrated transmission lines during the normal and stressed conditions of the system. The instantaneous current measurements available at either sending or receiving end are processed through EMD to decompose it into a series of intrinsic mode functions (IMFs) and IMF2 is identified as a dominated IMF with numerous case wise investigations. 1/4th cycle moving window is used to calculate the absolute sum of the IMF2 coefficients to detect the faults with the support of a predefined threshold. The efficacy of the method is tested on different types of faults during the normal condition in presence of wind and later extended to stressed conditions such as power swing. The method is reliable during the typical cases and includes remote end and high resistance faults. All the experiments are carried out in Simulink to generate the measurement data and programs are executed in MATLAB.
Volume: 14
Issue: 4
Page: 960-969
Publish at: 2025-12-01

Deep neural network solutions to Newell-Whitehead-Segel equations

10.11591/ijai.v14.i6.pp5172-5182
Soumaya Nouna , Ilyas Tammouch , Assia Nouna , Mohamed Mansouri
In this work, we use the deep neural network (DNN) approach called NeuroDiffEq, and the unified finite difference exponential approach for obtaining the approximated and exact solutions of Newell-Whitehead-Segel systems that are essential for the biology of mathematics. A unified approach was used to generate several solutions for solitary waves of those systems. The approximated solutions for selected studies are explored using the NeuroDiffEq approach, which is the artificial neural networks (ANN) approach and is based upon trial approximate solution (TAS). The comparison between the obtained approximated solutions and the analytical solutions indicates that the applied method has proved an efficient as well as a highly successful approach to solving various types of the Newell-Whitehead-Segel equations.
Volume: 14
Issue: 6
Page: 5172-5182
Publish at: 2025-12-01

A smart grid fault detection using neuro-fuzzy deep learning algorithm

10.11591/ijai.v14.i6.pp5096-5105
Etienne Francois Mouckomey , Jacques Bikai , Camille Franklin Mbey , Alexandre Teplaira Boum , Felix Ghislain Yem Souhe , Vinny Junior Foba Kakeu
This paper proposes a novel data analysis framework that integrates deep learning with a binary neuro-fuzzy algorithm to address the problem of fault localization in smart power grids. In the first stage, a long short-term memory (LSTM) network is employed to train data samples collected from smart meters. The resulting learned features are subsequently utilized by an adaptive neuro-fuzzy inference system (ANFIS) for accurate fault detection and classification. Through this intelligent hybrid approach, multi-phase faults can be efficiently identified using a limited amount of data. The proposed method distinguishes itself by its capacity to rapidly train and test large datasets while maintaining high computational efficiency. To evaluate the performance of the model, an advanced simulation of the IEEE 123-node test feeder is conducted. The robustness and effectiveness of the proposed framework are validated using multiple performance metrics, including precision, recall, accuracy, F1-score, computational complexity, and the ROC curve. The results demonstrate that the proposed deep learning–based model significantly outperforms existing approaches in the literature, achieving a fault detection and classification precision of 99.99%.
Volume: 14
Issue: 6
Page: 5096-5105
Publish at: 2025-12-01

Three-phase power flow solution of active distribution network using trust-region method

10.11591/ijape.v14.i4.pp923-933
Rudy Gianto , M. Iqbal Arsyad , Managam Rajagukguk
Distribution systems or networks are inherently unbalanced. As a result, single-phase power flow methods are generally no longer valid for such systems. Therefore, to obtain accurate results, unbalanced systems should be analyzed using three-phase power flow methods, which are far more complicated than the single-phase methods. Moreover, at present, the penetration of distributed generation (DG) in the distribution network has significantly increased. DG integration will increase the complication of the power flow analysis as it changes the network's basic configuration from passive to active system. This computational burden will significantly be higher if the power flow calculation has to be conducted several times (for example, in feeder reconfigurations or service restorations). This paper investigates the utilization of the trust-region method in obtaining the solution to the three-phase power flow problem of an active distribution network (i.e., distribution network embedded with DG). Trust-region computation algorithm is robust and powerful since the optimization technique is employed in finding new solutions in the iteration process. Results obtained from three representative unbalanced distribution networks (i.e., 10-node, 19-node, and 25-node networks) verify the validity of the proposed method. The effects of DG installation on distribution network steady-state performances are also investigated in the present paper.
Volume: 14
Issue: 4
Page: 923-933
Publish at: 2025-12-01

Design of miniaturized dual-band bandpass filter with enhanced selectivity for GPS and RFID applications

10.11591/ijict.v14i3.pp993-1001
Thupalli Shaik Mahammed Basha , Arun Raaza , Vishakha Bhujbal , Meena Mathivanan
This article presents a miniaturized interdigital coupled dual-band bandpass filter with multiple transmission zeros/poles. Stepped impedance resonators, interdigital coupled lines, and series coupled lines make up the proposed filter design. A circuit simulator is used to analyze a proposed filter, and the magnitude and bandwidth shifts have been investigated. To confirm the proposed filter design, equations for transmission zero frequencies have been constructed and verified based on even-odd mode analysis and lossless transmission line theory. A working prototype for 2.2 GHz (RFID) and 1.38 GHz (GPS) applications is made and tested. With λg representing the guided wavelength at the first band (1.38GHz), the finished prototype is compact, measuring 0.32 λg×0.27 λg. According to the experimental findings, there is strong selectivity in the first and second passbands, with roll-off rates of 190 and 168 dB/GHz, respectively. Good isolation between the two passbands is indicated by an insertion loss of less than 20 dB.
Volume: 14
Issue: 3
Page: 993-1001
Publish at: 2025-12-01

Deep learning-based evaluation for distributed denial of service attacks detection

10.11591/ijai.v14.i6.pp4982-4992
Neethu S. , H. V. Ravish Aradhya , Viswavardhan Reddy Karna
Software-defined network (SDN) introduces a programmable and centralized control mechanism for managing network infrastructure, enhancing flexibility and efficiency. However, this architecture is prone to security threats, particularly distributed denial of service (DDoS) attacks that exploit centralized control. This study presents a comparative analysis of several deep learning (DL) models—namely, multilayer perceptron (MLP), artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM)—for detecting DDoS threats within SDN environments. The research incorporates key preprocessing techniques such as feature selection and synthetic minority oversampling technique (SMOTE) to handle class imbalance. The results indicate that sequence-aware models like LSTM and RNN are highly effective in interpreting temporal network behavior, with LSTM achieving the highest performance (accuracy: 91%, precision: 86%, recall: 94%, and F1-score: 90%). These findings underscore the potential of advanced DL methods in fortifying SDN infrastructures against complex cyber threats.
Volume: 14
Issue: 6
Page: 4982-4992
Publish at: 2025-12-01

Frequency control of hybrid power system with fractional order secondary controller using improved biogeography-based krill herd algorithm

10.11591/ijape.v14.i4.pp816-825
Kukkamalla Kiran Kumar , Gobinathan Balaji , Kanta Rao Pedakota , Majahar Hussain Mahammad , Syed Suraya
To meet the demand of electrical power, structural changes of the power system from the generation side are necessary by integrating the renewable sources into the existing system. In the presence of renewables, the active power imbalances caused by both generation and demand are reduced with the classical units (like thermal) since the wind speed and irradiance (inputs of wind and solar plants) are volatile and nonlinear in nature. The frequency deviations triggered by such active power imbalances of the hybrid power system integrated with both conventional and renewable energy plants are minimized with better secondary control schemes. Therefore, this article suggests fractional order secondary controller (FOSC) for conventional units of the interconnected power system to strengthen the frequency stability of the system during the demand perturbations. The optimal gains of the FOSC are identified with an improved biogeography-based krill herd optimizer with the help of the performance indicator integral square error. To elevate the improvements of FOSC, comparisons are provided with classical controllers during the simple, random load perturbations with and without generation changes. Furthermore, sensitivity analysis on system parameters is performed to show the robustness of the FOSC over classical control strategies.
Volume: 14
Issue: 4
Page: 816-825
Publish at: 2025-12-01
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