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30,735 Article Results

Stability analysis of photovoltaic grid-connected power systems employing virtual synchronous generator control

10.11591/ijpeds.v17.i2.pp1451-1461
Abdallah El Ghaly , Abdullah Hamdan , Mohamad Tarnini
The rapid integration of photovoltaic (PV) systems into power networks poses significant challenges to grid stability, including reduced inertia, voltage fluctuations, and limited fault ride-through (FRT) capabilities. This study presents a comparative analysis of two inverter control strategies: the synchronous reference frame (SRF) controller and the virtual synchronous generator (VSG) controller. A high-fidelity MATLAB/Simulink model was developed, incorporating the effects of irradiance and temperature, maximum power point tracking (MPPT), and battery energy storage system (BESS) interaction. Standardized fault scenarios were applied at PV penetration levels ranging from 30% to 150% in accordance with IEEE-1547, IEEE-519, and IEC 61727 requirements. The results show that SRF control achieves superior harmonic suppression, with a total harmonic distortion (THD) consistently below 0.5%, confirming its suitability for strong grids prioritizing power quality. However, its stability deteriorated at higher penetration levels, with the voltage overshoot reaching approximately 16% and recovery times exceeding 3 s. In contrast, the VSG control demonstrates enhanced transient stability and effective FRT performance, with the overshoot limited to ≤5% and recovery achieved within 0.8 s across all operating conditions. The main contribution of this study lies in the direct benchmarking of the SRF and VSG control strategies under identical operating conditions using a unified evaluation framework, including an extended analysis beyond 100% PV penetration. The findings highlight a fundamental trade-off between harmonic performance and transient stability and provide practical guidance for selecting appropriate inverter control strategies for renewable-dominated power systems.
Volume: 17
Issue: 2
Page: 1451-1461
Publish at: 2026-06-01

Neuro-evolutionary genetic algorithm for global MPPT under partial shading conditions: a comparative analysis with PSO

10.11591/ijpeds.v17.i2.pp1499-1509
Benlaria Ismail , Laidi Abdallah , Fenniche Ayoub , Belhadj Mohammed
Maximizing power extraction from photovoltaic (PV) systems is crucial for their overall efficiency. However, under partial shading conditions (PSCs), the power-voltage curve shows several points of maximum power. This phenomenon often leads to traditional maximum power point tracking (MPPT) algorithms getting stuck at suboptimal local peaks, resulting in substantial energy losses. To solve this, we introduce a novel neuro-evolutionary genetic algorithm (NEGA) for global MPPT. This hybrid algorithm integrates a neural network to intelligently guide the evolutionary search process, improving its GMPP tracking. The performance of the NEGA controller is rigorously compared against the widely used particle swarm optimization (PSO) algorithm via MATLAB/Simulink simulations across various irradiance scenarios. Results under severe PSCs demonstrate NEGA's superior tracking efficiency of 98.69%, far exceeding PSO's 76.02%. Moreover, NEGA achieves a faster convergence time of 0.1 s under dynamic irradiance, compared to 0.6s for PSO. The study concludes that NEGA is a robust and highly efficient solution for global MPPT, ensuring maximum power harvesting from PV systems under challenging operating conditions.
Volume: 17
Issue: 2
Page: 1499-1509
Publish at: 2026-06-01

Design and implementation of a dual microcontroller-based smart headlight control system using a dynamic load adjustment mechanism

10.11591/ijpeds.v17.i2.pp1339-1354
Liew Hui Fang , Rosemizi Abd Rahim , Muhammad Izuan Fahmi Romli , A. A. M. Ezanuddin , Shamshul Bahar Yaakob
Conventional automotive headlamp systems operate using fixed illumination levels and manual beam levelling, limiting adaptability to dynamic driving conditions such as vehicle load variation, speed changes, and ambient light fluctuations. These static systems may result in reduced visibility, increased glare, and inefficient energy usage. This paper presents a dual microcontroller-based smart headlight control system incorporating a dynamic load adjustment mechanism for real-time regulation of beam intensity and angle. Unlike conventional single-controller configurations, the proposed architecture distributes control tasks between two dedicated microcontrollers to enhance modularity and processing stability. The first controller performs adaptive intensity regulation through speed-dependent low-beam dimming and LDR-based high-beam glare control, while the second controller enables automatic beam levelling using rear suspension load sensing to compensate for vehicle pitch variations. The system was validated through Proteus simulation and hardware prototyping. Experimental results demonstrate low-beam modulation at 30%, 80%, and 100% brightness levels, high-beam voltage control from 0.04 V to 1.82 V, and adaptive beam angle adjustments under varying load conditions. Approximately 90% simulation-to-hardware agreement confirms system reliability. Compared to conventional systems, the proposed design offers improved adaptive illumination, glare mitigation, and energy-aware operation, supporting integration into modern LED-based automotive lighting platforms and electric vehicles.
Volume: 17
Issue: 2
Page: 1339-1354
Publish at: 2026-06-01

Hybrid control strategy for trajectory tracking and obstacle avoidance in differential wheeled robots: integrating PSO-NMPC, GA, and fuzzy logic

10.11591/ijpeds.v17.i2.pp1008-1024
Abdennour Zeghida , Lotfi Farah , Halim Merabti , Abdelfateh Kerrouche
Mobile robots frequently encounter challenges in maintaining accurate trajectory tracking and effective obstacle avoidance in dynamic and uncertain environments. Traditional control methods, such as proportional integral derivative (PID) and standard MPC, often fail to provide the necessary adaptability and robustness for complex navigation tasks. To overcome these limitations, this study proposes a hybrid control framework for differential-drive wheeled robots that integrates particle swarm optimization–based nonlinear model predictive control (PSO-NMPC), adaptive neuro-fuzzy inference system (ANFIS) optimized by PSO, and genetic algorithm (GA) tuning. The PSO-NMPC computes optimal control inputs in real time while satisfying system constraints to ensure precise trajectory tracking, achieving an average RMSE of 0.0941 m (RMSEx = 0.0884 m, RMSEy = 0.0812 m). The ANFIS-PSO controller manages nonlinearities and environmental uncertainties for reliable obstacle avoidance, with an overall RMSE of 0.1084 m (RMSEx = 0.0761 m, RMSEy = 0.0772 m). The GA further optimizes key parameters and trajectories, ensuring global path refinement and robust obstacle clearance, achieving an overall RMSE of 0.1094 m (RMSEx = 0.1059 m, RMSEy = 0.0274 m). Simulation results in Matlab2024b confirm that the proposed hybrid framework provides precise trajectory tracking, smooth control, and robust obstacle avoidance, making it a promising solution for autonomous mobile robots operating in dynamic and uncertain environments.
Volume: 17
Issue: 2
Page: 1008-1024
Publish at: 2026-06-01

Innovative frequency and voltage controller for AC microgrid

10.11591/ijpeds.v17.i2.pp1486-1498
Xuan Hoa Thi Pham , Hai Van Tran
This paper designs a power controller for power converters using fuzzy logic. The proposed controller will automatically adjust the frequency and voltage when the load changes to improve the power quality of the microgrid. Besides, the controller can realize accurate power sharing among the power converters in the microgrid, thereby suppressing the circulating current between the inverters. Furthermore, to ensure the control system operates stably and accurately during voltage and frequency adjustments, this paper employs a sliding-mode controller rather than a conventional proportional-integral controller. The proposed control method has a voltage deviation from the rated value when the load changes in the range of 1.5 Volts to 2.7 volts, and a frequency deviation from the rated value when the load changes in the range of 0.2 to 0.4 Hz. The accuracy of reactive power division is 100%. The proposed controller is simulated using MATLAB/ Simulink software, and the results obtained from the simulation have verified the effectiveness of the proposed method.
Volume: 17
Issue: 2
Page: 1486-1498
Publish at: 2026-06-01

Real-time implementation and comparative analysis of fault-tolerant control strategies for induction motor drives

10.11591/ijpeds.v17.i2.pp894-907
Asmaa Hammou , Mokhtar Bendjebbar , Mohammed Benslimane
For nearly five decades, the induction motor has been the most widely used electrical machine in industry due to its robustness, simplicity, and low cost, supported by advances in power electronics enabling effective performance control. While DC motors were previously favored for their ease of speed and torque regulation, induction motors have gained prominence because they do not require brushes and involve fewer wear-prone components, resulting in reduced maintenance and improved reliability. Consequently, they are widely employed in industrial applications and emerging fields such as electric and hybrid vehicles. This study presents a comparative analysis of two fault-tolerant control (FTC) strategies: field-oriented control (FOC) and direct torque control (DTC). The evaluation focuses on sensitivity to parameter variations, dynamic performance, and steady-state behavior. Both strategies, classified under vector control techniques, are implemented in real time using a dSPACE platform to control an induction motor under an open-circuit fault in a two-level inverter. Results demonstrate that the DTC-based FTC approach offers superior robustness and stability compared to the IFOC-based method, particularly under fault conditions, load disturbances, and speed variations.
Volume: 17
Issue: 2
Page: 894-907
Publish at: 2026-06-01

Performance optimization of hybrid renewable energy systems with real-time load forecasting using grey wolf-based predictive models

10.11591/ijpeds.v17.i2.pp1382-1395
Olumuyiwa Ajibola Awoniyi , Evans Chinemezu Ashigwuike , Chijioke Ejimofor , Timothy Oluwaseun Araoye
The performance optimization of hybrid renewable energy systems (HRES) is crucial for enhancing the efficiency, reliability, and sustainability of energy production. This study focuses on the integration of real-time load forecasting prediction using a grey wolf optimization (GWO)-based predictive model. The proposed methodology aims to address the challenges associated with the intermittent nature of renewable energy sources, such as solar and wind power, by providing accurate forecasts for load demands and solar irradiance. Real-time data from sensors and environmental parameters are incorporated to forecast the energy load and solar irradiance over short-term periods, which are then used to optimize the energy storage and generation components of the HRES. The GWO algorithm, known for its high accuracy and computational efficiency, is employed to optimize the dispatch of power from various sources while minimizing energy losses and ensuring system stability. The integration of GWO with real-time forecasting not only enhances the predictive capability of the system but also improves the overall economic viability of HRES by reducing operational costs and carbon emissions. This study demonstrates the potential of using intelligent optimization techniques and real-time forecasting for the sustainable operation of hybrid renewable energy systems, contributing to the development of smarter and more resilient energy grids.
Volume: 17
Issue: 2
Page: 1382-1395
Publish at: 2026-06-01

Integration of wind energy with a single-ended primary inductor converter and a brushless DC motor for water pumping system

10.11591/ijpeds.v17.i2.pp1096-1104
Hassan Abdi Abi , Abdullahi Mohamed Isak , Suleiman Abdullahi Ali , Yakub Hussein Mohamed , Sowdo Mursal Abdi , Abdirisakh Khalif Osman
This paper explores a simulation-based study on a renewable energy system that integrates wind energy with a single-ended primary inductor converter (SEPIC) to drive a brushless DC (BLDC) motor for water pumping applications. The proposed system addresses the challenge of regulating the variable output of wind turbines by employing a SEPIC converter to provide a stable direct current (DC) voltage supply to the BLDC motor. The novelty of this work lies in the combined modeling and performance analysis of the wind turbine, SEPIC converter, BLDC motor, and electronic commutation in MATLAB/Simulink, optimized for energy-efficient off-grid pumping. Simulation results demonstrate that the SEPIC converter effectively stabilizes the wind-generated voltage, ensuring reliable motor operation under varying wind conditions. The proposed system exhibits high efficiency, stable dynamic response, and low maintenance requirements, making it a practical solution for water pumping in wind-rich regions where solar irradiance is limited, particularly for off-grid water pumping applications.
Volume: 17
Issue: 2
Page: 1096-1104
Publish at: 2026-06-01

Neural network approaches for quality-of-service optimization in software-defined networking environments

10.12928/telkomnika.v24i3.27766
Muqamuddin; Jawaharlal Nehru Technological University Hyderabad Muhib , Rangu; Jawaharlal Nehru Technological University Hyderabad Sridevi
Software-defined networking (SDN) enables centralized and programmable control of network behavior; however, conventional routing strategies remain largely reactive and struggle to adapt to rapidly changing traffic dynamics. To address this limitation, this study proposes a learning-based SDN routing framework that integrates a long short-term memory (LSTM) model to predict traffic patterns and proactively optimize routing decisions. The proposed approach is implemented and evaluated in an SDN testbed using realistic traffic scenarios. Experimental results are averaged over multiple independent runs to ensure robustness and reproducibility. Compared with static shortest-path routing and classical machine learning (ML) baselines, the proposed model demonstrates consistent improvements in latency, packet loss, and throughput under the evaluated conditions. In particular, the ablation study reports a 95% confidence interval for end-to-end latency ranging from 51.8 to 55.6 ms, confirming the statistical stability of the observed gains. Additional analyses show that the framework maintains low inference latency and modest control overhead, making it suitable for real-time SDN environments. Overall, the findings indicate that temporal learning models can effectively enhance SDN routing performance when evaluated within controlled experimental settings, offering a practical pathway toward more adaptive and intelligent network control.
Volume: 24
Issue: 3
Page: 825-839
Publish at: 2026-06-01

Comprehensive assessment and analysis of frequency fluctuation and voltage total harmonics distortion in Malaysia’s grid-connected solar PV systems: an empirical study

10.11591/ijpeds.v17.i2.pp1426-1439
Hasif Mohamad , Khairul Anwar Ibrahim , Che Wan Mohd Faizal Che Wan Mohd Zalani , Zulkifli Ibrahim , Mohd Nor Hasli Mat Jusoh
Grid-connected solar photovoltaic (GCPV) systems have become an essential part of modern electricity generation due to their ability to harness clean, renewable energy, reduce greenhouse gas emissions, and lower dependence on fossil fuels. In Malaysia, initiatives promoting small-scale GCPV adoption among residential, commercial, and industrial users have been notably successful. However, concerns regarding power quality (PQ) within GCPV-integrated environments remain insufficiently explored. This study presents a comprehensive evaluation of the impact of GCPV generation on frequency fluctuations and voltage total harmonic distortion (THDV) within the Malaysian grid. The methodology involves empirical measurements of PQ at a selected GCPV installation, focusing on frequency fluctuation and THDV, and compares the results against Malaysian and international standards. These measurements form the basis for further statistical analysis, which includes descriptive analysis, process capability analysis, and Pearson correlation analysis. The study aims to provide insights into grid stability, the influence of GCPV output on PQ, and the relationship between environmental factors and PQ deviations. Findings reveal that GCPV generation has minimal impact on grid PQ, which remains within acceptable limits set by relevant standards. Furthermore, no significant correlation was observed between GCPV output and PQ deterioration. The results contribute to a deeper understanding of PQ challenges in GCPV systems and offer valuable guidance for regulators and utility providers to support the development of effective mitigation strategies to ensure the continued stability and efficiency of Malaysia’s evolving power grid.
Volume: 17
Issue: 2
Page: 1426-1439
Publish at: 2026-06-01

Adaptive control of the virtual synchronous generator by deep neural networks for a wind high power conversion chain

10.11591/ijpeds.v17.i2.pp1440-1450
Wijdane El Maataoui , Abdelouahed Abounada
The virtual synchronous generator (VSG) is commonly used to reproduce the inertial response of conventional synchronous machines. However, the VSG control architecture relies on controller chains, benchmark transformations, and parameter settings, including virtual inertia and damping, which limit its flexibility in highly dynamic environments. This paper proposes an innovative end-to-end control approach based on a neural network to fully replace the classical VSG control structure. The neural network developed is trained to directly generate inverter control signals from real-time electrical measurements, including voltages and currents, as well as active and reactive power. A dataset is generated from a detailed VSG model under different operating conditions, and then a multilayer neural network is trained using supervised learning with MATLAB. The resulting model is then integrated into a complete wind energy conversion chain simulated in Simulink. The simulation results demonstrate that control based on artificial neural networks ensures better frequency and voltage stability, more accurate tracking of the active power injected, and a significant improvement in power quality, with total harmonic distortion (THD) reduced to 0.04%, compared to 0.51% for conventional VSG control. These results confirm the potential of artificial intelligence-based approaches for the intelligent control of renewable energy systems.
Volume: 17
Issue: 2
Page: 1440-1450
Publish at: 2026-06-01

High step-up interleaved multilevel hybrid boost converter with switched-capacitor multiplier

10.11591/ijpeds.v17.i2.pp1118-1129
Andi M. Nur Putra , Adrianti Adrianti , Muhammad Imran Hamid
The global integration of renewable energy sources like photovoltaics requires efficient high-step-up DC-DC converters. Conventional boost converters exhibit inherent limitations in achieving high voltage gain efficiently, particularly under high duty cycle operation, where switching losses, device stress, and output voltage ripple become significant. This paper proposes a novel hybrid DC-DC converter that integrates a four-phase interleaved input stage with a five-level switched-capacitor (SC) multiplier network. The proposed topology introduces a modular and structurally decoupled architecture, in which current conditioning and voltage boosting functions are independently realized. This enables scalable voltage gain through modular expansion without requiring extreme duty cycles or additional magnetic components. The interleaved stage reduces input current ripple and improves current sharing, while the multilevel SC network provides a high voltage conversion ratio and balanced voltage stress across components. Comprehensive simulations using PSIM software validate the converter's performance. With a 25 V input, the proposed converter achieves an output voltage of approximately 250 V (gain of 10), a high efficiency of 95.2%, output voltage ripple below 2%, and balanced capacitor voltages. The results confirm that the proposed converter offers an efficient, scalable, and high-performance solution for high step-up applications.
Volume: 17
Issue: 2
Page: 1118-1129
Publish at: 2026-06-01

Enhanced review on dynamic real-time digital simulation analysis of renewable energy integration using state space model

10.11591/ijpeds.v17.i2.pp1510-1521
Ahmad Supawi Osman , Aidil Azwin Zainul Abidin
The modernization of electric power grids, driven by communication and electronic hardware advances alongside increasing renewable energy integration, introduces challenges like voltage fluctuations, weakened protection, and transient instability. High renewable penetration can trigger reverse power flow and voltage rise, complicating system control. Real-time digital simulations offer a non-destructive approach to analyze and optimize power system behavior under diverse conditions. Using platforms like Simulink Real-Time and RT-LAB with OPAL-RT, detailed studies of protection relays, circuit breakers, and control algorithms are efficiently conducted. This paper reviews real-time digital simulation techniques for renewable-integrated power systems, emphasizing state-space modeling for capturing system dynamics. Recent developments in predictive and event-based control strategies to enhance microgrid stability and operational efficiency are examined. Simulations of a three-bus system with transient analysis and event-based predictive control for energy management are discussed, demonstrating how real-time simulation platforms support renewable energy integration while maintaining grid stability.
Volume: 17
Issue: 2
Page: 1510-1521
Publish at: 2026-06-01

Proximal policy optimization-based type II PPC for EV fast charging

10.11591/ijpeds.v17.i2.pp835-848
Franco Aldrin Joseph Menezes , Gopala Reddy Krishnappa
In recent years, efficient and fast charging is critical for accelerating the adoption of electric vehicle (EV). However, traditional fully rated converters process the total power flow to the battery, but leading to excessive thermal stress, high energy losses, and quick battery degradation. Similarly, existing partial power converter (PPC) designs like type I and type II PPC, improve efficiency by processing only a fraction of the total power; however, they still face challenges such as additional isolation requirements, limited step-down performance, and lack of advanced control for fluctuating state of charge (SoC) conditions. To overcome these challenges, this research proposes a proximal policy optimization (PPO)-enhanced type II PPC for fast EV charging. Initially, the power is routed through a low-frequency (LF) isolation transformer and filtered to mitigate high-frequency noise. A portion of the power is partially processed through a SiC MOSFET-based phase-shifted full-bridge converter, while the remaining power bypasses directly to the battery. The PPO controller efficiently adjusts the phase shift angle in real time, optimizing switching cycles to reduce switching and thermal losses. The proposed PPO-type II PPC achieved better results in terms of peak efficiency (99.36%) and partial power handling (12.21%) when compared to existing type II PPC designs.
Volume: 17
Issue: 2
Page: 835-848
Publish at: 2026-06-01

Design and performance evaluation of a soft-switched partial-power LLC converter for PV grid integration

10.11591/ijpeds.v17.i2.pp1130-1141
Sebin Davis Kurichiparambil , Varghese Jegathesan
This paper presents a soft-switched partial-power LLC converter integrated within a two-stage photovoltaic (PV) and grid-connected system. The proposed architecture combines the advantages of resonant operation and partial power processing to enhance conversion efficiency and reduce switching losses. Maximum power point tracking (MPPT) is achieved through frequency modulation of the LLC converter, while grid synchronization is maintained using a three-phase voltage-oriented control (VOC) inverter. Simulation results in MATLAB/Simulink demonstrate stable zero voltage switching (ZVS) and zero current switching (ZCS) across a wide irradiance range (400-1000 W/m²), enabling the system to achieve peak efficiencies above 98%, which is superior to typical transformerless and interleaved converter topologies reported in recent literature. The proposed soft-switched PPC-LLC architecture offers an efficient and scalable solution for next-generation PV grid interfaces by combining reduced processed power, robust resonant operation, and high-quality grid integration.
Volume: 17
Issue: 2
Page: 1130-1141
Publish at: 2026-06-01
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