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Comparative study of fuel economy and emissions for plug-in hybrid electric Payang Water Taxi on different driving cycles using ADVISOR

10.11591/ijpeds.v17.i1.pp25-36
Ahmad Luqmanul Hakim Ahmad Tarmizi , Siti Norbakyah Jabar , Salisa Abdul Rahman
A new conceptual series-parallel plug-in hybrid vehicle for water transportation, known as the plug-in hybrid electric Payang Water Taxi (PHEPWT), is designed to improve vehicle fuel economy and significantly lower boat emissions. This article aims to analyze the fuel economy and emissions of PHEPWT, which are Hydrocarbons (HC), Carbon Monoxide (CO), and Nitrogen Oxides (NOx), with 6 driving cycles including Pulau Warisan river route, Kuala Terengganu river route, Kampung Laut river route, Seberang Takir river route, Pulau Kapas river route, and Tasik Kenyir river route. The analysis of the PHEPWT model will be compared with the existing powertrain architectures using water drive cycles by using the advanced vehicle simulator (ADVISOR). The results will be expected based on the fuel economy and emissions analysis that will show about 30-50% improvement in driving cycle for each driving cycle, and the fuel economy of the PHEPWT will indicate about 15-20% higher than that of the ADVISOR model. Also, for emission, the PHEPWT and ADVISOR models are based on the result of three-type emission such as HC, CO, and NOx, and show that the PHEPWT model has a lower emission compared to the ADVISOR model.
Volume: 17
Issue: 1
Page: 25-36
Publish at: 2026-03-01

High efficient DC-AC inverter for low wireless power transfer applications

10.11591/ijpeds.v17.i1.pp453-464
Kyrillos K. Selim , Hanem Saied Ebrahem Torad , Mostafa R. A. Eltokhy , Hesham F. A. Hamed , Mohamed Elzalik
The inverter's simplicity is an important aspect that must be considered especially for electronic devices, as adding the number of power switches increases the complexity and overall cost of the inverter. This work proposes an inverter design that converts DC into AC power. It receives 12 VDC as an input voltage, and it is composed of a boost converter that converts an input voltage of 5-20 VDC to an output voltage of 4-30 VDC and a pulse width modulation controller to produce a square wave with a frequency of 100 kHz to drive the switching MOSFET. The designed inverter can be operated on different loads ranging from 50 Ω to 1000 Ω, tested in both simulations and experimentally. The design was optimized by the LT Spice simulator. The proposed inverter has operating frequencies ranging from 40 kHz to 110 kHz, taking into account different loads. The obtained results showed that both simulation and experimental results converged, whereas the highest efficiency was 96.96% at 55 kHz at a fixed load of 100 Ω. On the other hand, the maximum achieved efficiency when the load was sweeping was 80% at a load of 50 Ω at a fixed frequency of 100 kHz.
Volume: 17
Issue: 1
Page: 453-464
Publish at: 2026-03-01

Application of machine learning for production optimization and predictive maintenance in an iron processing plant

10.11591/ijpeds.v17.i1.pp765-776
Lakhdari Lahcen , Mohamed Habbab , Alhachemi Moulay Abdellah
The modern metallurgical industry requires advanced solutions for process optimization, cost reduction, and predictive maintenance. This paper proposes a unified simulation-based framework using machine learning (ML) to jointly address production optimization and maintenance prediction in a virtual iron processing environment. Several ML models, including random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector machine (SVM), and k-nearest neighbors (k-NN), were evaluated on synthetic datasets representing production, maintenance, and transport processes. A reproducible methodology was adopted, including preprocessing, time-aware data splitting, and cross-validation to prevent information leakage. Model performance was assessed using F1-score, area under the receiver operating characteristic curve (AUC), and regression metrics. Tree-based models achieved near-perfect classification performance (AUC ≈ 1, precision and recall > 0.99), while light gradient boosting machine (LightGBM) and CatBoost provided the best regression accuracy. Feature importance analysis using SHapley Additive exPlanations (SHAP) identified vibration and temperature as key maintenance indicators. Although based on simulation, the framework is designed for integration with supervisory control and data acquisition (SCADA) and the Industrial Internet of Things (IIoT), supporting real-time industrial deployment and alignment with operational key performance indicators.
Volume: 17
Issue: 1
Page: 765-776
Publish at: 2026-03-01

Optimizing small-scale geothermal power: insights from long-term testing and system modifications of a 3 MW geothermal condensing power plant in Kamojang, Indonesia

10.11591/ijpeds.v17.i1.pp709-719
Lina Agustina , Suyanto Suyanto , Budi Ismoyo
This study presents the design, development, and performance evaluation of a 3 MW geothermal pilot power plant in Kamojang, Indonesia, developed by retrofitting a 2 MW backpressure turbine into a six-stage condensing turbine. With a 63.81% local content, the plant serves as one of Indonesia’s first demonstrations of small-scale condensing turbine technology. Multi-phase testing yielded a maximum net output of 2.2 MW, below the design target due to condenser vacuum inefficiencies, strainer pressure losses, and reduced turbine isentropic efficiency. Subsequent condenser and strainer modifications improved vacuum stability, reduced pressure drops, and enhanced specific steam consumption (SSC) and overall performance. Exergy analysis identified the condenser (16.1%) and turbine (9.5%) as the primary sources of exergy destruction, resulting in an overall exergy efficiency of 73.6%, higher than typical small-scale geothermal benchmarks. While operational performance improved significantly, sustaining long-term vacuum stability and optimizing turbine operation under variable steam conditions remain key challenges. Future work should focus on automated vacuum control, real-time monitoring, and advanced thermodynamic-electrical optimization to enhance system reliability. This study provides practical insights into turbine retrofitting, condenser stabilization, and integrated exergy evaluation, contributing to the advancement and localization of small-scale geothermal power technology in Indonesia.
Volume: 17
Issue: 1
Page: 709-719
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

Mitigating harmonic distortion in grid-connected PV systems: a comparative evaluation of ANFIS and IC-based MPPT techniques

10.11591/ijpeds.v17.i1.pp303-316
Bouledroua Adel , Mesbah Tarek , Kelaiaia Samia
Integrating photovoltaic (PV) systems into power grids presents notable challenges related to power quality, especially concerning total harmonic distortion (THD) caused by power electronic converters, which do not comply with IEEE 519 and IEC 61000 standards. This study introduces an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) controller designed to optimize energy extraction while concurrently mitigating harmonic distortion in three-phase grid-connected PV systems. Unlike conventional IC-MPPT methods, which compromise the quality of the power to monitor performance, the proposed ANFIS-MPPT strategy uses intelligent modulation index adjustment to achieve a dual goal. A comparison of the four irradiation levels (450-1000 W/m²) shows a higher performance: ANFIS-MPPT achieves 0.26% THD at 1000 W/m² compared to 0.44% at IC-MPPT (40.9% improvement), and 0.80% versus 1.12% at 450 W/m² (28.6% reduction). The five-layer ANFIS architecture, trained on temperature and radiation data, shows faster convergence and lower oscillation than the conventional approach. The results confirm that MPPT based on ANFIS is an effective solution to improve the energy quality of grid-integrated photovoltaic installations while maintaining optimal energy conversion efficiency.
Volume: 17
Issue: 1
Page: 303-316
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

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

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

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

A framework for robust PID controller design: an optimization-based approach for inductive loads

10.11591/ijpeds.v17.i1.pp359-369
Ali Abderrazak Tadjeddine , Miloud Kamline , Latifa Smail , Soumia Djelaila , Hafidha Reriballah
This paper presents a comprehensive comparative study of proportional-integral-derivative (PID) controller tuning methodologies for inductive load applications across three representative scenarios. We systematically evaluate classical methods (Ziegler-Nichols, internal model control) against global optimization algorithms (genetic algorithm (GA), particle swarm optimization (PSO)) applied to resistor-resistor-inductor (RRL) circuit models. Results demonstrate that PSO achieves superior performance for moderate-to-slow systems, reducing settling time by 84% while completely eliminating overshoot compared to Ziegler-Nichols. The algorithm automatically discovers optimal PI controller structures, simplifying implementation. However, for ultra-fast systems (time constants < 1 ms), internal model control proves more reliable, achieving 0.84 ms settling with only 0.16% overshoot. Optimized controllers demonstrate exceptional robustness, maintaining stability under ±50% parameter variations and effectively rejecting disturbances. This research provides engineers with a scenario-based framework for method selection, moving beyond heuristic tuning to achieve previously unattainable performance levels. The findings establish optimization-based tuning as a systematic, reliable approach for high-performance control system design in industrial applications.
Volume: 17
Issue: 1
Page: 359-369
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

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

Improving photovoltaic efficiency: a systematic study of P&O and INC MPPT techniques

10.11591/ijpeds.v17.i1.pp728-739
Abdelkbir Jamaa , Ahmed Moutabir , Rachid Marrakh , Abderrahmane Ouchatti
Achieving high efficiency in photovoltaic (PV) systems under fluctuating irradiance and temperature conditions relies on effective maximum power point tracking (MPPT) techniques. Among the most commonly adopted approaches, perturb and observe (P&O) and incremental conductance (INC) are favored for their ease of implementation and operational flexibility. Nevertheless, a systematic comparison of their performance under dynamic conditions remains limited. This study conducts a comparative evaluation of P&O and INC algorithms using MATLAB/Simulink, with emphasis on tracking accuracy, convergence speed, and overall efficiency. A standard PV module is exposed to rapid variations in irradiance and temperature to examine algorithm robustness. The results indicate that although P&O achieves fast convergence in steady-state operation, it exhibits noticeable oscillations around the maximum power point, resulting in efficiency losses of up to 3%. Conversely, the INC method offers improved tracking precision and reduced oscillations, yielding efficiency gains of 2-4% over P&O in dynamic environments. These findings underline the trade-off between algorithmic simplicity and tracking accuracy, and provide practical guidance for selecting MPPT strategies in both grid-connected and standalone PV applications. 
Volume: 17
Issue: 1
Page: 728-739
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
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