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Adaptive intelligent PSO-Based MPPT technique for PV systems under dynamic irradiance and partial shading conditions

10.11591/ijpeds.v16.i4.pp2841-2859
Muhammad Gul E. Islam , Mohammad Faridun Naim Tajuddin , Azralmukmin Azmi , Rini Nur Hasanah , Shahrin Md. Ayob , Tole Sutikno
This research introduces an adaptive improved particle swarm optimization (AIPSO) approach for maximum power point tracking (MPPT) approach designed to enhance energy harvesting from photovoltaic (PV) systems under dynamic irradiance conditions. The proposed AIPSO algorithm addresses the challenges associated with traditional MPPT methods, particularly in scenarios characterized by fluctuating solar irradiance, such as step changes and partial shading. By incorporating a robust reinitialization strategy along with updated velocity and position equations, the algorithm demonstrates superior performance in terms of convergence accuracy, tracking speed, and tracking efficiency. This modification enables the algorithm to effectively escape local maxima and explore a wider search space, leading to improved convergence and optimal power point tracking. Furthermore, the adaptive nature of the PSO enhances the algorithm’s ability to respond to real-time changes in environmental conditions, making it particularly suitable for large- scale PV systems subjected to varying atmospheric factors. Here, “adaptive” denotes coefficient scheduling (C3) and a re-initialization trigger that responds to irradiance regime changes; “intelligent” denotes robust regime shift detection and safe duty ratio clamping. Across uniform, step change, and partial shading conditions, the proposed AIPSO achieves fast reconvergence and high tracking efficiency with negligible steady state oscillations, as summarized in the results. Building on this contribution, future research will focus on evaluating its scalability across different PV architectures and large-scale grid integration with real hardware setup.
Volume: 16
Issue: 4
Page: 2841-2859
Publish at: 2025-12-01

Enhanced voltage stability in power distribution networks through optimal reconfiguration using hybrid metaheuristic algorithms

10.11591/ijpeds.v16.i4.pp2582-2591
Mohammed Zuhair Azeez , Abbas Swayeh Atiyah , Yaqdhan Mahmood Hussein , Hatem Oday Hanoosh
An optimal network reconfiguration (ONR) is used in distribution power systems to improve voltage decreases within the permitted period and minimize real power losses. Consequently, attaining optimal reconfiguration in distribution systems is regarded as the primary objective of numerous researchers. Conventional heuristic techniques such as genetic algorithms (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) can reduce active power losses and enhance network stability. These algorithms indicate a greater number of difficulties, including inadequate convergence characteristics, a reduction in power loss, and an increase in bus voltage. This research proposes effective optimization strategies utilizing the salp swarm algorithm (SSA) and whale optimization algorithm (WOA) to augment bus voltage, reduce distribution losses, and improve network dependability. The proposed algorithms are executed and evaluated on the IEEE 33-bus and 69-bus networks to determine the ideal network architecture. The efficacy of the examined methodologies is illustrated through MATLAB under steady-state conditions, showcasing benefits in the reduction of active power loss relative to current algorithms. The comparison indicates that the SSA algorithm exhibits superior performance in terms of power losses and bus voltage enhancement relative to the WOA method. due to its enhanced exploration and exploitation capabilities, which help avoid local optima and ensure a more effective search for optimal solutions. SSA's adaptive mechanism and cooperative behavior improve convergence speed and solution accuracy, making it more efficient for optimization in network reconfiguration.
Volume: 16
Issue: 4
Page: 2582-2591
Publish at: 2025-12-01

Predictions of solar power using ensemble machine learning techniques

10.11591/ijpeds.v16.i4.pp2868-2878
Arangarajan Vinayagam , R. Mohandas , R. Jeyabharath , B. S. Mohan , Srinivasan Lakshmanan , C. Bharatiraja
Predicting solar power production accurately is becoming more and more crucial for efficient power management and the grid's integration of renewable energy sources. Using data from an Australian photovoltaic (PV) power station, this study employs a variety of machine learning (ML) ensemble techniques, such as gradient boosting (GB), random forest (RF), and extreme gradient boosting (XGBoost), to forecast solar power production. ML models are developed utilizing pertinent information from electricity and meteorological data in order to forecast solar power. The predictive performance of trained ML models is verified in terms of metrics like mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R2). With higher R2 values and lower error results (MAE and RMSE), XGBoost performs better than GB and RF. Optimizing the hyperparameters of the XGBoost model significantly improves its performance. The tweaked XGBoost model shows a significant improvement in R2 (more than 5% to 10%) and error results (reduced MAE and RMSE by 0.01 to 0.06), when compared to other ensemble approaches. Compared to other ensemble approaches, the tuned XGBoost methodology is more robust and generates more accurate forecasts in solar power.
Volume: 16
Issue: 4
Page: 2868-2878
Publish at: 2025-12-01

ANN-based MPPT for photovoltaic systems: performance analysis and comparison with nonlinear and classical control techniques

10.11591/ijpeds.v16.i4.pp2780-2791
Khadija Abdouni , Mostafa Benboukous , Drighil Asmaa , Hicham Bahri , Mohamed Bour
In photovoltaic energy systems, maximum power point tracking (MPPT) techniques are essential for optimizing power output under changing climatic conditions. Several techniques have been proposed in the literature, including classical techniques such as perturb and observe (P&O) and incremental conductance (INC), nonlinear controllers such as backstepping, and artificial intelligence-based techniques like fuzzy logic. This study compares the performance of an artificial neural network (ANN)-based MPPT approach with these nonlinear and classical MPPT techniques. It analyses the advantages and limitations of the various techniques to evaluate their performance in terms of efficiency, accuracy, and output power stability under changing climatic conditions. The study aims to help researchers select the most effective technique to improve the efficiency of photovoltaic systems. The simulation was carried out using MATLAB/Simulink. The simulation results indicated that the artificial neural network achieved better performance than the other techniques in terms of tracking speed, with an efficiency of up to 99.94%, while maintaining stable output power under changing climatic conditions. The backstepping controller also showed stable output power compared to traditional techniques. Fuzzy logic had a lower efficiency than both the artificial neural network and backstepping. Perturbation and observe and incremental conductance are easy to implement, but they showed oscillations around the maximum power point, which reduces the overall efficiency of the system.
Volume: 16
Issue: 4
Page: 2780-2791
Publish at: 2025-12-01

Dynamic modelling and small-signal analysis of an efficient bridge-type multi-input DC converter for hybrid low-power systems

10.11591/ijpeds.v16.i4.pp2441-2452
Baya Reddy Lomada , Vangala Naga Bhaskar Reddy
This paper presents the dynamic modelling and small-signal analysis of a bridge-type multi-input DC converter designed for hybrid low-power systems. The converter architecture supports the integration of multiple energy sources such as fuel cells and photovoltaic (PV) arrays, enabling enhanced flexibility and reliability. A CUK-based configuration is employed to achieve continuous input current and reduced voltage stress across the switches. The dynamic behavior of the converter is analyzed through average large-signal and small-signal state-space modelling. Stability is assessed using the Routh-Hurwitz criterion, and steady-state analysis is carried out to support performance evaluation. Experimental results obtained from a 250 W prototype confirm the validity of the developed models and demonstrate the efficiency and suitability of the proposed converter for hybrid renewable energy systems.
Volume: 16
Issue: 4
Page: 2441-2452
Publish at: 2025-12-01

Adaptive fuzzy logic controller based BLDC motor to improve the dynamic performance for electric tractor application

10.11591/ijpeds.v16.i4.pp2186-2196
Ashwini Yenegur , Mungamuri Sasikala
Permanent magnet brushless DC (PMBLDC) motors are widely used in a variety of industrial applications due to their high-power density and ease of regulation. The three-phase power semiconductors bridge is the standard way for controlling these motors. In order to initiate the inverter bridge and switch on the power devices, rotor position sensors must be provided with the correct commutation sequence. The power devices commutate progressively 60 degrees, depending on the location of the rotor. The right speed controllers are necessary for the motor to run as efficiently as possible. PI controllers are commonly employed with permanent magnet motors to achieve speed control in simple manner. Nevertheless, these controllers provide challenges in managing control complexity, including nonlinearity, parametric fluctuations, and load disturbances. PI controllers need accurate linear mathematical models. To overcome this, in this paper adaptive fuzzy logic controller (FLC) for controlling the speed of a BLDC motor is presented. When the motor drive system uses the adaptive FLC technology for speed control, it exhibits better dynamic behavior and is more resistant to changes in parameters and load disturbances. The main objectives of this work are to analyze and appraise the functioning of an electric tractor driven by a PMBLDC motor drive using adaptive FLC. The PMBLDC motor drive controllers are simulated using MATLAB/Simulink software.
Volume: 16
Issue: 4
Page: 2186-2196
Publish at: 2025-12-01

Performance placement of BESS in the Sulawesi-Southern interconnected power system

10.11591/ijpeds.v16.i4.pp2819-2830
Zaenab Muslimin , Indar Chaerah Gunadin , Fitriyanti Mayasari , Muhira Dzar Faraby , Asma Amaliah , Isminarti Isminarti
Frequency regulation and active power loss management are crucial aspects of power system operations. Battery energy storage systems (BESS) have emerged as an innovative solution to enhance grid performance, especially in addressing frequency fluctuations and reducing power losses. This study explores the role of BESS in optimizing frequency regulation and managing active power losses in the power system through several BESS integration scenarios. In this study, a BESS with a capacity of 8.437 MW was used and analyzed using symmetric steady-state simulations in DigSILENT PowerFactory software. The simulations aim to test the effectiveness of BESS in frequency regulation and minimizing active power losses in the Sulbagsel system. The analysis results show that implementing BESS can respond effectively to both over-frequency and under-frequency conditions in the Sulbagsel system. In the discharge scenario, BESS can reduce the system's average frequency by 0.02 Hz and decrease active power losses by up to 1.09 MW. Conversely, in the charge scenario, active power losses increase by 1.22 MW when the BESS is installed on Bus Tonasa. This study provides valuable insights for developing BESS-based frequency regulation strategies that contribute to the stability and efficiency of the power system.
Volume: 16
Issue: 4
Page: 2819-2830
Publish at: 2025-12-01

SCADE: a deep learning ensemble for semantic flow analysis in smart contract vulnerability detection

10.11591/ijeecs.v40.i3.pp1417-1429
Muralidhara Srirama , Usha Banavikal Ajay
A vulnerability in smart contracts refers to weaknesses in the code that can be exploited by attackers, leading to security breaches and unintended behavior. With the growing use of smart contracts in decentralized blockchain systems, particularly in internet of things (IoT) environments, ensuring their security has become increasingly critical. Traditional vulnerability detection techniques, such as formal verification and symbolic execution, face significant limitations, including high rates of false positives and negatives, scalability issues, and difficulty in detecting complex vulnerabilities. To address these challenges, this paper proposes semantic contract flow analysis and deep learning ensemble (SCADE) for smart contract vulnerability detection. SCADE leverages semantic flow analysis combined with an ensemble of deep learning models, including convolutional neural networks (CNN), bidirectional sequence encoder (BSE), layered probabilistic neural network (LPNN), and adaptive context learning network (ACLN), to detect vulnerabilities effectively. The methodology breaks down the smart contract code into structured components through a contract structure mapper, followed by extracting semantic paths and converting them into sequential vector representations. These representations are then processed through a deep learning ensemble to identify potential vulnerabilities such as reentrancy, timestamp dependency, code injection, and hardcoded gas amounts.
Volume: 40
Issue: 3
Page: 1417-1429
Publish at: 2025-12-01

Implementation of adaptive PID control for maintaining temperature stability during steady-state conditions in stirred heating tank

10.11591/ijpeds.v16.i4.pp2389-2399
Pricylia Valentina , Hendro Tjahjono , Agus Sunjarianto Pamitran , Iwan Roswandi , Putut Hery Setiawan , Arif Adtyas Budiman , Dedy Haryanto , Sanda Sanda , Kukuh Prayogo , Mulya Juarsa
Temperature stability is a crucial factor in industries such as chemicals, pharmaceuticals, and food processing, where fluctuations can damage product quality and increase energy consumption. This study aims to optimize heater power control using an adaptive proportional integral derivative (PID) control system to maintain temperature stability under steady-state conditions. The method involves applying adaptive PID control to a stirred heating tank using LabVIEW software with a national instruments controller module and a single-phase SCR to regulate heater power and adjust control parameters in real time. The results indicate that the system operates more effectively under stable conditions, with faster response times and a lower overshoot of less than 0.12%. However, under disturbed conditions, such as water drainage and replacement, the system requires more time to adjust the temperature and experiences increased energy consumption and heat loss. Despite this, the system still achieves an energy efficiency improvement, with efficiency values ranging from 77.66% to 80.03%. The implementation of adaptive PID control demonstrates significant potential in enhancing system accuracy and response to temperature changes, contributing to the development of more efficient industrial control technologies.
Volume: 16
Issue: 4
Page: 2389-2399
Publish at: 2025-12-01

Comparative evaluation of PVGIS, PVsyst, and SAM models for predicting solar power output in equatorial tropical climates

10.11591/ijeecs.v40.i3.pp1221-1231
Fabian Alonso Lara Vargas , Miguel Angel Ortiz Padilla , Alvaro Torres Amaya , Carlos Vargas Salgado
Accurate evaluation of energy production in photovoltaic (PV) systems is critical for renewable projects, especially in tropical climates where environmental factors such as temperature significantly affect performance. Although commercial simulation tools exist (photovoltaic geographic information system (PVGIS), PVsyst, and system advisor model (SAM)), previous studies have identified notable deviations between their predictions and actual data, particularly in tropical climates. Moreover, these investigations are usually limited to short periods (one year) and do not systematically compare multiple tools under interannual conditions. This study evaluates the accuracy of PVGIS, PVsyst, and SAM in predicting the energy production of a PV installation in a tropical equatorial climate for 24 months to identify the most suitable tool for this context. Monthly energy production data were collected from a PV plant in Monteria, Colombia, equipped with 240 modules and two 36 kW inverters. Simulations were performed using the most recent PVGIS, PVsyst, and SAM versions. Accuracy was evaluated using metrics such as root mean square error (RMSE) and mean absolute error (MAE). SAM showed the highest accuracy, with an overall RMSE of 1,993.71 kWh and MAE of 1,615.87 kWh, followed by PVGIS (RMSE: 2,076.65 kWh, MAE: 1,830.84 kWh) and PVsyst (RMSE: 3,546.18 kWh, MAE: 3,250.17 kWh). The results highlight that SAM provides estimates closer to the real data and less dispersion than other tools. This study contributes to the renewable energy field by systematically comparing simulation tools in an understudied tropical context. The findings emphasize the importance of selecting appropriate software according to the specific environmental conditions of the project, thus optimizing the design and efficiency of PV systems in tropical regions.
Volume: 40
Issue: 3
Page: 1221-1231
Publish at: 2025-12-01

Study of asymmetrical-multi level inverter using two switching angle techniques

10.11591/ijpeds.v16.i4.pp2570-2581
Dewan Ashikur Rahaman , Tapan Kumar Chakraborty
An inverter is a device that transforms DC power into AC power. Inverters can be categorized into single-level inverters and multilevel inverters. This paper discusses two controlled strategies-equal step angle and sinusoidal switching angle-for a multilevel inverter, highlighting their effectiveness in harmonic mitigation as the number of voltage levels increases. The simulation software used to generate 3-15 level voltage outputs is PSIM, which allows for the adjustment of switching angles based on both equal step and sinusoidal switching values. Various types of DC sources are connected to H-bridge units, with MOSFET driving signals applied via gating blocks. The study demonstrates a notable reduction in total harmonic distortion (THD) when the switching angles are altered in equal and sinusoidal steps. Initially, the output signal generates a square wave without a filter. However, after implementing an LC filter, the output voltage signal more closely resembles an AC signal, and THD values are further reduced. Additionally, the output voltage signal's fast Fourier transform (FFT) is presented.
Volume: 16
Issue: 4
Page: 2570-2581
Publish at: 2025-12-01

Support-centric PSO-based fuzzy MPPT tuning for photovoltaic systems under uniform conditions

10.11591/ijpeds.v16.i4.pp2792-2803
Amel Smaili , El-Ghalia Boudissa , M’hamed Bounekhla
Several conventional maximum power point tracking (MPPT) algorithms have been applied to harvest the optimal power of a photovoltaic (PV) system. However, the main drawbacks of these algorithms are their fluctuations around the maximum power point (MPP) and their dependence on climatic conditions variation. To overcome these issues, a fuzzy logic controller (FLC) is proposed, where the system performance depends strongly on the choice of membership functions (MFs). They are typically selected by trial and error, which may not always yield the best results. This paper seeks to enhance the efficiency of the traditional FLC method by using the particle swarm optimization (PSO) algorithm for optimizing the supports of the triangular MFs. The simulation was performed using MATLAB-Simulink environment using the "1Soltech 1STH-215-P" PV module and a single-ended primary-inductor converter (SEPIC) converter, under ideal environmental conditions of 25 °C and 1000 W/m². A comparison is established between PSO-optimized FLC and the standard FLC-based MPPT method, as well as with several other state-of-the-art approaches reported in related research. The simulation data present that the PSO-optimized FLC approach outperforms other algorithms.
Volume: 16
Issue: 4
Page: 2792-2803
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

Evaluation of midwifery educated mobile applications for labor guidance and a roadmap for future developers

10.11591/ijai.v14.i6.pp5268-5278
Seeta Devi , Swapnil Vitthal Rahane , Lily Podder , Sangeetha X. , Kumari Dimple
The objective of the study was to review the midwifery guided mobile apps for labor advice, assessing features, functions, and content relevance. In February to March 2024, midwifery labor-guided applications were reviewed in mobile platforms such as the Google Play Store and Apple iTunes Store. We used multimodal evaluation tools, such as the mobile app rating scale (MARS), specific statements, and IQVIA ratings, to assess the quality of these applications. The study evaluated midwifery-guided applications, resulting in an average objective quality score of 3.96±0.96 out of 5. 'Safe delivery' scored the highest rating of 4.94, followed by 'Pregnancy mentor' (4.89), 'Hypno-birthing' (4.61), 'Obstetrics 6th edition' (4.68), and 'MSD manual guide to obstetrics' (4.56). Functionality received the highest score (4.16±0.865), followed by information (3.99±0.97), engagement (3.88±1.07), and aesthetics (3.82±0.28) areas. Subjective quality score was 3.6±1.18 out of 5 for an overall MARS score of 3.76±1.02. Most applications received favorable reviews, indicating good quality, and it is recommended that future app developers design applications that include comprehensive information on labor management.
Volume: 14
Issue: 6
Page: 5268-5278
Publish at: 2025-12-01

Speed control of 3-phase induction motor with modified DTC using HTAF-ANN

10.11591/ijpeds.v16.i4.pp2197-2211
Arpita Banik , Raja Gandhi , Chandan Kumar , Achyuta Nand Mishra , Rakesh Roy
In this research paper, an artificial neural network (ANN) algorithm is implemented with modifications to enhance the performance of a direct torque controlled (DTC) induction motor drive. Since the main challenge in the conventional DTC technique is to tune the PI controller appropriately therefore in this work, an ANN technique is incorporated in place of the conventional PI controller. Sudden changes in speed and loading in induction motor drives lead to sharp fluctuations and disturb the motor performance. In order to overcome these issues, a trained ANN controller is initially used here to enhance motor drive performance. Subsequently, the performance is further improved by modifying the activation function in the ANN controller. Here, motor parameters at rated and variable speed with various loading conditions have been analyzed and compared for the DTC with a conventional PI controller with ANN, and a proposed ANN controller. Simulation of the complete model with the conventional and proposed controllers is done using MATLAB/Simulink platform to observe the various speed responses for different conditions, and the experimental setup is used to demonstrate the effectiveness and performance of the proposed system.
Volume: 16
Issue: 4
Page: 2197-2211
Publish at: 2025-12-01
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