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Enhancing the dynamic stability of electric power systems through the coordinated tuning of generator predictive controllers

10.11591/ijpeds.v17.i1.pp211-222
Hristo Beloev , Yuri Bulatov , Andrey Kryukov , Konstantin Suslov , Yuliya Valeeva , Magdalena Dudek , Iliya Iliev
The paper presents a method for the coordinated tuning of automatic voltage regulation (AVR) and automatic speed control (ASC) systems for a group of generators operating in parallel at a power plant. The method also involves solving the optimization problem using a genetic algorithm. The possibilities of using lead-lag elements in AVR and ASC, which impart predictive properties and improve damping characteristics of the controllers, are also considered. A model of a power plant operating in parallel with an electric power system is presented. This model demonstrates effective damping of oscillations under large disturbances when the proposed method is used to adjust the AVR and ASC control coefficients, along with a self-tuning lead-lag element. In this case, voltage oscillations and frequency overshoot disappear, and there is a significant reduction in the maximum deviations of these parameters. In the illustrative case study, the coordinated tuning of the controllers provides a 6% increase in the transmitted power limit and, as a consequence, the enhancement of the stability margin of the electric power system.
Volume: 17
Issue: 1
Page: 211-222
Publish at: 2026-03-01

A novel approach to flexible BTMS for 2-wheeler electric vehicle to avoid fire accidents-an Indian perspective

10.11591/ijpeds.v17.i1.pp49-57
Mahmooda Mubeen , Gangishetti Srinivas
The Indian electric vehicles market share has significantly increased due to various government initiatives, increased fuel prices, and charging infrastructure. On the contrary, fire accidents of EV’s in India are no rarer due to inappropriate BTMS and its inability to work with different environmental conditions prevailing in India, so it has become one of the major concerns. Two-wheelers, being one of the most used modes of transport, are dominating the Indian roads; it well deserves an innovative BTMS that suits local environmental conditions for preventing thermal runaways and maintaining better performance of the battery. As we get to see diverse environmental conditions at different parts of India, it will be good if we can develop flexible BTMS. Major challenges being faced in the development of suitable BTMS are space and cost constraints. This paper focuses on the development of BTMS for electric two-wheelers, suitable for various environmental conditions, which fits in the available space with low additional cost. It also provides flexibility to drop or add some of the features based on one’s operational requirements or environmental conditions prevailing at the place of operation, which can be as easy as one can drop or choosing to have fog lamps, speakers, camera, and sunroof depending upon their requirement and budget.
Volume: 17
Issue: 1
Page: 49-57
Publish at: 2026-03-01

Voltage compensation using fuel cell fed dynamic voltage restorer

10.11591/ijpeds.v17.i1.pp663-673
Ryma Berbaoui , Rachid Dehini
One of the basic tasks of the dynamic voltage restorer (DVR) is to maintain voltage stability in distribution systems by correcting any deviations or disturbances in the three-phase supply. Whether they are increases or decreases. However, one of its disadvantages is its power source, as it cannot supply itself with power from the electrical grid like parallel compensators, which obtain power directly from the grid. This article presents an energy study of a dynamic voltage regulator (DVR) when operated using a power source represented by fuel cells, which are considered a clean and renewable source. On the other hand, excess energy from the regenerator or fuel cells can be output and injected into the distribution network for utilization via a parallel compensator (CP). The parallel compensator also compensates for reactive energy on the reactive load side to increase the power factor measured at the source side of the distribution system. This integrated system also uses neural networks to identify voltage disturbances and determine the voltages (modules/arguments) that must be added to the voltages in the power grid for correction. This analytical study was completed using a simulation system to confirm the effectiveness of this integrated system. The distinctive feature of this study is the integration of fuel cells and neural network-based control in the DVR system, providing a sustainable and intelligent alternative to conventional configurations, which makes it different from traditional DVRs that operate with batteries and supercapacitors. Its efficiency in compensating for voltage drops and surges is evident, and it also improves the power factor and ensures reliable operation of voltage-sensitive devices.
Volume: 17
Issue: 1
Page: 663-673
Publish at: 2026-03-01

Study of neural controller based MPPT in comparison with P&O for PV systems

10.11591/ijpeds.v17.i1.pp797-808
Djaafar Toumi , Mourad Tiar , Abir Boucetta , Ikram Boucetta , Ahmed Ibrahim
This study investigated the performance of two prominent maximum power point tracking (MPPT) strategies: the established perturb and observe (P&O) technique and an artificial neural network (ANN)-based controller. Through simulations conducted in MATLAB/Simulink, a 50 W photovoltaic (PV) array was evaluated under dynamic irradiance and temperature variations. Notably, data generated by the P&O system served as the training dataset for the ANN model. The simulation results indicate that the ANN controller effectively and accurately identifies the PV system’s optimal operating point even amidst fluctuating environmental conditions. When compared to the conventional P&O method, the ANN approach demonstrated superior characteristics, including a significantly faster response, diminished oscillations around the maximum power point, and enhanced tracking accuracy during rapid environmental shifts. These findings underscore the substantial potential of ANN-based MPPT strategies for improving both the efficiency and operational stability of photovoltaic power systems.
Volume: 17
Issue: 1
Page: 797-808
Publish at: 2026-03-01

Ferrite-based magnetic shielding for efficiency enhancement in resonant inductive wireless power transfer systems

10.11591/ijpeds.v17.i1.pp572-581
Wan Muhamad Hakimi Wan Bunyamin , Rahimi Baharom
This paper presents a detailed simulation-based investigation of ferrite-based magnetic shielding to enhance the efficiency and electromagnetic performance of resonant inductive wireless power transfer (RIPT) systems, with a particular emphasis on electric vehicle (EV) wireless charging applications. Two system configurations, a baseline coil-only system and a ferrite-shielded system, were modelled and simulated using CST Studio Suite 3D electromagnetic simulation software under identical geometric and electrical conditions to ensure a fair comparative evaluation. Key performance metrics, including power transfer efficiency (PTE), H-field distribution, and magnetic flux confinement, were analyzed to quantify the shielding impact. The ferrite-shielded configuration achieved a PTE improvement from 98.29% to 99.01%, demonstrating stronger flux concentration, reduced leakage, and lower electromagnetic interference (EMI) exposure. Additional analyses highlight the trade-offs in ferrite integration, including potential core loss, material cost, and thermal drift, while also discussing the system’s robustness against coil misalignment and its alignment with SAE J2954 and IEC 61980 standards for EV charging. The study is limited to a simulation-based approach without experimental validation; however, the findings establish a solid foundation for future hardware prototyping and hybrid shielding exploration, integrating ferrite and composite or metamaterial-based structures. Overall, this work contributes to the development of efficient, EMI-compliant, and thermally stable WPT systems suitable for next-generation EV charging infrastructures.
Volume: 17
Issue: 1
Page: 572-581
Publish at: 2026-03-01

Solar power forecasting using a SARIMA approach for Indonesia's grid integration

10.11591/ijpeds.v17.i1.pp293-302
Ricky Maulana , Syafii Syafii , Aulia Aulia
Indonesia’s transition toward a renewable energy-dominated power grid is progressing to meet increasing energy demands while reducing dependence on fossil fuels. According to the National Energy General Plan, their goal is to have 23% of the energy mix come from renewables by 2025 and 31% by 2050. Accurate forecasting of photovoltaic (PV) power output is crucial to address the intermittent nature of solar energy and ensure grid stability. A seasonal autoregressive integrated moving average (SARIMA) model was developed to estimate day-ahead photovoltaic power output in Padang City, Indonesia. Using NASA solar irradiance data from March 1-31, 2024, the SARIMA(1,0,1)(4,0,3)24 model achieved high accuracy with an NRMSE of 4.19%. To evaluate its performance, a comparative evaluation was conducted between the SARIMA model and two machine learning methods, namely artificial neural network (ANN) and long short-term memory (LSTM), in which SARIMA achieved the lowest forecasting error. These findings indicate that SARIMA remains an effective and interpretable statistical method for short-term PV forecasting, supporting reliable energy planning and power grid operations towards Indonesia's renewable energy goals.
Volume: 17
Issue: 1
Page: 293-302
Publish at: 2026-03-01

D-STATCOM control for distribution grids with distributed sources based on MMC structure using FCS-MPC algorithm

10.11591/ijpeds.v17.i1.pp425-437
Pham Viet Phuong , Le Hoai Nam , Pham Chi Hieu , Tran Hung Cuong
This paper proposes a D-STATCOM structure based on a modular multilevel converter (MMC) with the use of FCS-MPC control method for the purpose of compensating reactive power and stabilizing voltage in the distribution grid. The D-STATCOM is effectively used in cases involving non-sinusoidal and unstable voltages, which often occur in the distribution grid due to the effects of unbalanced nonlinear loads and power injection from renewable energy systems. The proposed structure also has the capability of reactive power compensating flexibility in fault conditions to stabilize the grid voltage. In this paper, a new control strategy, which is based on the combination of an outer PI controller and an inner FCS-MPC controller, was introduced. The outer PI controller is used to reduce static deviations in control values and to provide a reference value for the FCS-MPC controller. The inner FCS-MPC controller calculates the optimal switching state for the purpose of reducing the switching frequency of the MMC. The implementation process begins with the construction of a mathematical model and a control model. Simulations were carried out by MATLAB/Simulink to demonstrate the responsiveness of the control algorithm and the performance of D-STATCOM under the conditions of non-sinusoidal and unstable voltages.
Volume: 17
Issue: 1
Page: 425-437
Publish at: 2026-03-01

A comparative analysis of PoS tagging tools for Hindi and Marathi

10.11591/ijict.v15i1.pp120-137
Pratik Narayanrao Kalamkar , Prasadu Peddi , Yogesh Kumar Sharma
Many tools exist for performing parts of speech (PoS) data tagging in Hindi and Marathi. Still, no standard benchmark or performance evaluation data exists for these tools to help researchers choose the best according to their needs. This paper presents a performance comparison of different PoS taggers and widely available trained models for these two languages. We used different granularity data sets to compare the performance and precision of these tools with the Stanford PoS tagger. Since the tag sets used by these PoS taggers differ, we propose a mapping between different PoS tagsets to address this inherent challenge in tagger comparison. We tested our proposed PoS tag mappings on newly created Hindi and Marathi movie scripts and subtitle datasets since movie scripts are different in how they are formatted and structured. We shall be surveying and comparing five parts of speech taggers viz. IMLT Hindi rules-based PoS tagger, LTRC IIIT Hindi PoS tagger, CDAC Hindi PoS tagger, LTRC Marathi PoS tagger, CDAC Marathi PoS tagger. It would also help us evaluate how the Bureau of Indian Standards’s (BIS) tag set of Indian languages compares to the Universal Dependency (UD) PoS tag set, as no studies have been conducted before to evaluate this aspect.
Volume: 15
Issue: 1
Page: 120-137
Publish at: 2026-03-01

A novel hybrid PI and adaptive super-twisting sliding mode controller for high-performance integrated speed and flux regulation of IMDs

10.11591/ijpeds.v17.i1.pp414-424
Duc Thuan Le , Ngoc Thuy Pham
This paper presents a novel hybrid control strategy that integrates a proportional-integral (PI) regulator with an adaptive super-twisting sliding mode controller (ASTA) defined on a nonsingular terminal sliding mode control (NTSMC) surface for high-performance induction motor drives (IMDs). This enhanced hybrid PI-ASTA-NTSMC architecture jointly exploits the steady-state accuracy of PI control and the finite-time robustness of a higher-order sliding mode formulation. The adaptive mechanism of the super-twisting algorithm dynamically adjusts the switching gains according to the instantaneous sliding variable, ensuring consistent performance under time-varying loads and parameter variations. The NTSMC surface guarantees singularity-free finite-time convergence, while the adaptive ASTA law suppresses chattering and enhances disturbance rejection. Simulation results across multiple operating conditions show that the proposed controller significantly outperforms PI and PI-FOSMC schemes. It achieves the fastest transient, reducing settling time to 0.0407 s (39.4% and 31.5% faster than PI and PI-FOSMC), with overshoot lowered to 0.0091 rad/s and ISE/IAE minimized to 0.0035 and 0.0256, confirming its superior tracking precision. Additionally, reductions in the speed and torque RMSE indicate smoother control effort and improved closed-loop performance. The Lyapunov-based analysis confirms global finite-time stability of the overall system. With its enhanced robustness, low sensitivity to sampling noise, and continuous higher-order sliding structure that suppresses chattering, the proposed hybrid PI–ASTA–NTSMC offers a computationally efficient and practically attractive solution for integrated speed–flux control in industrial IM drives.
Volume: 17
Issue: 1
Page: 414-424
Publish at: 2026-03-01

Enhanced smart farming security with class-aware intrusion detection in fog environment

10.11591/ijict.v15i1.pp257-266
Selvaraj Palanisamy , Radhakrishnan Rajamani , Prabakaran Pramasivam , Mani Sumithra , Prabu Kaliyaperumal , Rajakumar Perumal
The adoption of the internet of things (IoT) in smart farming has enabled real-time data collection and analysis, leading to significant improvements in productivity and quality. However, incorporating diverse sensors across large-scale IoT systems creates notable security challenges, particularly in dynamic environments like Fog-to-Things architectures. Threat actors may exploit these weaknesses to disrupt communication systems and undermine their integrity. Tackling these issues necessitates an intrusion detection system (IDS) that achieves a balance between accuracy, resource optimization, compatibility, and affordability. This study introduces an innovative deep learning-driven IDS tailored for fog-assisted smart farming environments. The proposed system utilizes a class-aware autoencoder for detecting anomalies and performing initial binary classification, with a SoftMax layer subsequently employed for multi-class attack categorization. The model effectively identifies various threats, such as distributed denial of service (DDoS), ransomware, and password attacks, while enhancing security performance in environments with limited resources. By utilizing the Fog-to-Things architecture, the proposed IDS guarantees reliable and low-latency performance under extreme environmental conditions. Experimental results on the TON_IoT dataset reveal excellent performance, surpassing 98% accuracy in both binary and multi-class classification tasks. The proposed model outperforms conventional models (convolutional neural network (CNN), recurrent neural network (RNN), deep neural network (DNN), and gated recurrent unit (GRU)), highlighting its superior accuracy and effectiveness in securing smart farming networks.
Volume: 15
Issue: 1
Page: 257-266
Publish at: 2026-03-01

Leveraging distillation token and weaker teacher model to improve DeiT transfer learning capability

10.11591/ijict.v15i1.pp198-206
Christopher Gavra Reswara , Gede Putra Kusuma
Recently, distilling knowledge from convolutional neural networks (CNN) has positively impacted the data-efficient image transformer (DeiT) model. Due to the distillation token, this method is capable of boosting DeiT performance and helping DeiT to learn faster. Unfortunately, a distillation procedure with that token has not yet been implemented in the DeiT for transfer learning to the downstream dataset. This study proposes implementing a distillation procedure based on a distillation token for transfer learning. It boosts DeiT performance on downstream datasets. For example, our proposed method improves the DeiT B 16 model performance by 1.75% on the OxfordIIIT-Pets dataset. Furthermore, we present using a weaker model as a teacher of the DeiT. It could reduce the transfer learning process of the teacher model without reducing the DeiT performance too much. For example, DeiT B 16 model performance decreased by only 0.42% on Oxford 102 Flowers with EfficientNet V2S compared to RegNet Y 16GF. In contrast, in several cases, the DeiT B 16 model performance could improve with a weaker teacher model. For example, DeiT B 16 model performance improved by 1.06% on the OxfordIIIT-Pets dataset with EfficientNet V2S compared to RegNet Y 16GF as a teacher model.
Volume: 15
Issue: 1
Page: 198-206
Publish at: 2026-03-01

Smart accommodation solution: innovative boarding house locator in Bayombong municipality

10.11591/ijict.v15i1.pp1-12
Carmelo Alejo D. Bisquera , Vilchor G. Perdido , Napoleon Anthony M. Mendoza
The search for affordable and conveniently located student accommodation is a common challenge, especially for students unfamiliar with their surroundings. This study presented the development and evaluation of a geographical information system (GIS)-enabled boarding house locator developed for Nueva Vizcaya State University (NVSU) students. The platform simplified the accommodation search process by providing a digital solution that integrates spatial data, real-time updates, and filtering options. The platform significantly reduced the time and cost of traditional housing searches. It helped students save 181.25 minutes per search and an average of 35 PHP in transportation costs compared to conventional methods like physical visits and word-of-mouth. Usability testing with 175 participants revealed high satisfaction, with the platform receiving an average rating of 4.83 for usability and 4.75 for performance. Key features such as interactive maps, location-based searches, and real-time updates enhanced the user experience by providing accurate, and up-to-date listings. The GIS-based platform outperformed traditional search methods in terms of efficiency and user satisfaction and offered a digital solution to common housing challenges faced by students. The results suggested the platform had strong potential for wider application at other universities. Overall, this system provides a scalable, cost-effective solution to improve student accommodation search and management.
Volume: 15
Issue: 1
Page: 1-12
Publish at: 2026-03-01

Mapping academic outcomes to student routines using machine learning: a data-driven approach

10.11591/ijict.v15i1.pp66-73
Selvakumar Venkatachalam , Pillalamarri Lavanya , Shreesh V. Deshpande , R. J. Akshaya Shree , S. V. Thejaswini
In today’s environment, students often struggle with time management and dealing with emotions like frustration and anxiety, which may have an adverse impact on their academic achievement. This research aims to enhance time management and educational support for college students by leveraging demographic characteristics and performance in specific assignments to develop a predictive model for academic performance. The study evaluates various regression algorithms to identify the most accurate method for predicting students’ semester grade point average (SGPA) based on their activities. This predictive model aims to optimize students’ learning experiences and mitigate challenges such as frustration and anxiety. The findings highlight the potential of personalized educational assistance in improving student learning outcomes. Various machine learning algorithms, including decision trees, support vector regression (SVR), ridge regression, lasso regression, XGBoost, and gradient boosting, were implemented in Python for this study. Results show that XGBoost achieved the lowest root mean square error (RMSE) of 9.39 with a 60:40 data split ratio, outperforming other algorithms, while decision trees exhibited the highest RMSE. The findings emphasize the potential of personalized educational assistance to improve learning outcomes by helping students adjust study habits to address weaknesses and reduce anxiety. Future studies can explore integrating real-time data and additional features such as emotional wellbeing and extracurricular activities to further improve the model’s predictive capabilities.
Volume: 15
Issue: 1
Page: 66-73
Publish at: 2026-03-01

A novel high-gain DC-DC converter with fuzzy logic control for hydrogen fuel cell vehicle applications

10.11591/ijpeds.v17.i1.pp617-628
Gaddala Anusha , A. V. V. Sudhakar , Shaik Rafikiran , Ram Ragotham Deshmukh , C. H. Hussaian Basha
Hydrogen fuel cell vehicles (HFCVs) are emerging as a sustainable alternative to conventional internal combustion engines due to their zero-emission characteristics and high energy efficiency. However, the low output voltage of fuel cells poses a significant challenge in meeting the high-voltage requirements of electric traction systems. To address this, this paper proposes a high-gain non-isolated switched-capacitor (SC) DC-DC converter integrated with a fuzzy logic controller (FLC) for efficient power management in hydrogen fuel cell vehicle applications. The proposed converter topology achieves a significant voltage step-up without the use of bulky magnetic components, making it lightweight and compact for automotive integration. A maximum power point tracking (MPPT) controller using fuzzy logic is used to recover optimum energy out of the fuel cell stack during different loads and conditions of the environment. MATLAB/Simulink simulation results validate the high voltage gain, stable operation, and improved dynamic response of the proposed converter under FLC control. The proposed intelligent control strategy enhances fuel cell utilization and ensures effective operation of HFCV powertrains.
Volume: 17
Issue: 1
Page: 617-628
Publish at: 2026-03-01

Hybrid renewable energy for cold chain in Indonesia: technical and economic evaluation

10.11591/ijpeds.v17.i1.pp674-682
I Made Aditya Nugraha , I Gusti Made Ngurah Desnanjaya , Anis Khairunnisa , Mahaldika Cesrany
Cold storage plays a crucial role in preserving temperature-sensitive products, particularly in the fisheries and food sectors. However, its operation is highly energy-intensive and often constrained by unstable electricity supply in many Indonesian regions. This study quantitatively evaluates a hybrid renewable energy system integrating photovoltaic (PV) panels, diesel generators, batteries, and the utility grid to ensure sustainable cold storage operations. Using measured load profiles, solar irradiation data, and annual operating costs, the system achieved a 60% reduction in diesel fuel consumption, 30-50% lower CO₂ emissions, and annual savings exceeding IDR 100 million compared to conventional generator-based systems. The system demonstrated 83.5% overall efficiency, with a payback period of 4.4 years and a positive net present value (NPV), confirming its economic viability. The novelty of this research lies in presenting the first comprehensive techno-economic analysis of a PV-diesel-battery-grid hybrid system specifically designed for fisheries-based cold storage facilities in Indonesia, considering local solar potential and grid reliability. Despite its feasibility, implementation challenges remain, including a lack of skilled technicians, limited financial incentives, and bureaucratic constraints. To overcome this, the study recommends PV subsidies, low-interest green loans, and public–private partnerships aligned with Indonesia's energy transition roadmap and cold chain development goals.
Volume: 17
Issue: 1
Page: 674-682
Publish at: 2026-03-01
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