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30,376 Article Results

Optimization of principal component analysis and k-nearest neighbors in cultivation area classification red onion

10.12928/telkomnika.v23i6.27103
Arif; Politeknik Negeri Medan Ridho Lubis , Purwa; Politeknik Negeri Medan Hasan Putra , Fahdi; International Islamic University Malaysia Saidi Lubis
This research aims to increase the effectiveness in classifying shallot cultivation areas through the combined application of principal component analysis (PCA) and k-nearest neighbors (KNN) methods. Shallot is an important agricultural commodity, and identification of optimal areas for its cultivation is essential to support food self-sufficiency. Onion cultivation is generally done in the highlands. One of the areas with shallot cultivation in North Sumatra Province is Berastagi, Karo Regency. This research was conducted by determining the spatial extent of upland land. In the use of data there are 2 types of data that will be used: land suitability dataset and land condition dataset for each region. The PCA method is utilized to simplify the data structure by reducing the number of dimensions and removing insignificant attributes, while KNN was used to classify regions based on their suitability for shallot cultivation. This research produces a classification map that can be used to identify the most optimal areas for shallot cultivation. The test results with the regional spatial dataset using precision, recall and fi-score testing accuracy value 0.92%, and macro avg value 0.94%, weighted avg value 0.93%.
Volume: 23
Issue: 6
Page: 1579-1589
Publish at: 2025-12-01

Advanced control architectures for enhanced simulation and operational analysis of solar PV-driven vehicle systems

10.11591/ijpeds.v16.i4.pp2615-2622
Raghupathi Mani , Susitra Dhanraj , Karthikeyan Nagarajan
Interplanetary interest in solar PV systems in automobiles has grown as renewable energy, especially in transportation subsystems, is used more widely. Emphasizing innovative control strategies to increase power conversion efficiency, reliability, and flexibility, this paper identifies and assesses solar photovoltaic integrated vehicle drive systems. In Simulink, several researchers replicate power systems, solar PV systems, vehicle propulsion systems, and power conversion technologies. To imitate real-world settings, researchers evaluate the efficiency of the device at many solar light and load values. High-level control techniques suitable in such unpredictable conditions are MPPT and dynamic load control. These controls are definitely required to ensure the correct functioning of the plant system, independent of natural variables, like irradiation and temperature. After that, the performance of the suggested control strategies is investigated under the main success criteria: energy analysis, system efficiency, and operational stability. This implies that solar PV integrated systems for automobiles could gain from ideal performance and durability, hence improving the off-grid operation of cars. These findings offered latent promise for use in the developing transportation sector and advancement of solar PV technology.
Volume: 16
Issue: 4
Page: 2615-2622
Publish at: 2025-12-01

Design and implementation of IoT-based soft starter for induction motor

10.11591/ijpeds.v16.i4.pp2170-2177
Laith Najem Abood Khudhur , Amer Abdulmahdi Jabbar Chlaihawi
The practical application of the induction motor is an essential part of electrical engineering. A direct connection of the motors to the mains voltage negatively affects both the motor itself and the mains system as a whole due to high starting current values, as a result, more accidents and shortening the drive system service life. This article discusses the development of designing and implementing of soft starter single-phase IM to reduce the inrush current using the firing angle reduction technique with remote monitoring and control using the ESP32 (node MCU) and Arduino Due microcontrollers. The integration of IoT-based tools software such as VS Code, enables the remote monitoring and control of motor features. Testing shows that the system effectively facilitates remote motor control, providing a flexible and accessible learning environment with minimum starting current, solving the inrush current problem facing IMs. The proposed soft starter gives three cases of firing angle reduction that show a percentage reduction in starting current for these cases (case I, case II, and case III) are 51%, 54% and 64%, respectively. Case III has a maximum starting current is 2.2 A compared to 6.2 A for direct connecting of IM to the power supply (DOL).
Volume: 16
Issue: 4
Page: 2170-2177
Publish at: 2025-12-01

Advanced thermal modeling of lithium-ion batteries: foundations for advanced capacity prediction

10.11591/ijpeds.v16.i4.pp2699-2710
Abdelhadi Elkake , El Mehdi Laadissi , Meriem Mossaddek , Tabine Abdelhakim , Abdelowahed Hajajji
Thermal modeling of lithium-ion batteries is crucial for optimizing their performance and reliability in applications such as electric vehicles and energy storage systems. This study introduces a novel thermal modeling framework to predict internal battery temperature as a function of current and ambient temperature. Three advanced methodologies, NN-LM, NN-BR, and GPM, were evaluated using drive cycle data across temperatures that vary from -20 °C to 25 °C. Among these, Gaussian process modeling (GPM) demonstrated the highest accuracy with an RMSE of 0.034%, while NN LM achieved an RMSE of 0.083%, offering a computationally efficient alternative suitable for real-time applications. The developed thermal model establishes a foundation for future research aimed at predicting battery capacity by incorporating the effects of internal temperature. Furthermore, accurate monitoring of internal temperature is critical for preventing thermal runaway by enabling early detection of unsafe thermal conditions. This work establishes a robust foundation for future research, aiming to develop real-time capacity prediction models, ultimately enhancing battery management systems under diverse operating conditions.
Volume: 16
Issue: 4
Page: 2699-2710
Publish at: 2025-12-01

Lithium-ion battery charge-discharge cycle forecasting using LSTM neural networks

10.11591/ijpeds.v16.i4.pp2831-2840
Vimala Channapatana Srikantappa , Seshachalam Devarakonda
An important component for the dependable and safe utilization of lithium-ion batteries is the ability to accurately and efficiently predict their remaining useful life (RUL). In this research, a long short-term memory recurrent neural network (LSTM RNN) model is trained to learn from sequential data on discharge capacities across different cycles and voltages. The model is also designed to function as a cycle life predictor for battery cells that have been cycled under varying conditions. By leveraging experimental data from the NASA battery dataset, the model achieves a promising level of prediction accuracy on test sets consisting of approximately 200 samples.
Volume: 16
Issue: 4
Page: 2831-2840
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

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

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

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

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

Assessment of the efficiency and performance of different PV system configurations under various fault conditions

10.11591/ijpeds.v16.i4.pp2744-2756
Raghad Adeeb Othman , Omar Sharaf Al-Deen Yehya Al-Yozbaky
Partial shadowing, bypass-diode issues, photovoltaic (PV) module deterioration, and wiring issues are examples of PV failures that have a substantial effect on power production and cause distinct peaks in a PV system's P-V curves. Various PV fault types have been used in the solar cell system in this work. Four types were used: open circuit, line to ground, cross-line to line, and intra-line to line. The impact of various PV system failure types on the system's performance was emphasized in this study. MATLAB is used to display the simulation results for the four approaches (series parallel (SP), total cross tied (TCT), honeycomb (HC), and bridge link (BL)) under various fault scenarios. The current-voltage (I-V) and power-voltage (P-V) curves are used to compare the results for each fault scenario. The open circuit fault between PV (7.8) in the first string and PV (18.19) in the fourth string resulted in a 40% decrease in the short-circuit current of the photovoltaic system compared to its normal value in the SP topology, while in the HC and BL topologies, the current value exceeded the allowable limit. This, in turn, had an impact on the (I-V) characteristics of this topology. The fault's impact was minimal and within the typical bounds of its (I-V) characteristics in the TCT topology.
Volume: 16
Issue: 4
Page: 2744-2756
Publish at: 2025-12-01

Improvement of DSIM control using fuzzy third-order sliding mode approach optimized by MOA

10.11591/ijpeds.v16.i4.pp2321-2331
Rahma Belkaid , Lamia Youb , Farid Naceri , Ghoulem Allah Boukhalfa
This study focuses on the contribution of a new hybrid controller based on the sliding mode technique associated with fuzzy logic and optimized by an innovative approach called the mayfly optimization algorithm (MOA) to improve the drive of the dual star induction motor (DSIM). The performance and robustness of this system are analyzed under different operating conditions with three proposed strategies and compared with each other under the MATLAB/Simulink environment. Through the simulation results obtained, we realize that the method that integrates the MOA with a hybrid controller associating the third order sliding mode with fuzzy logic (MOA-FTOSMC) makes a significant contribution to research work in this field and offers the best dynamic performance and adequately manages the uncertainty and variation of the system parameters under different operating regimes.
Volume: 16
Issue: 4
Page: 2321-2331
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

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

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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