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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

Enhanced UPS inverter control using backstepping and fuzzy neural network for improved power quality

10.11591/ijpeds.v17.i2.pp1069-1083
G. Anjali Devi , Swapna Ganapaneni , L. Sirisaiah , Lokesh Kotha , Subhash Manchikanti , Malligunta Kiran Kumar , T. Rakesh , K. V. Govardhan Rao
The rapid growth of sensitive digital infrastructures and automation systems has intensified the demand for uninterrupted and high-quality power delivery. To address this critical need, this paper proposes a novel hybrid intelligent control strategy for uninterruptible power supply (UPS) inverters that integrates backstepping control, fuzzy neural network (FNN) adaptation, and sliding mode gain compensation. The proposed approach ensures superior voltage regulation and robustness under nonlinear and dynamic load conditions while minimizing dependence on predefined system parameters. The backstepping controller establishes the Lyapunov-based stability framework, the FNN adaptively estimates system uncertainties in real time, and the sliding mode gain enhances resilience against external disturbances. This synergistic control integration enables fast dynamic response, reduced harmonic distortion, and improved system efficiency compared to conventional methods. Simulation and experimental validations demonstrate that the proposed controller achieves total harmonic distortion (THD) below 3%, voltage overshoot under 2%, and enhanced transient recovery, thereby ensuring reliable power quality for critical industrial and commercial applications. The study contributes a real-time feasible, adaptive, and robust UPS inverter control architecture, marking a significant advancement in intelligent power electronics for resilient energy systems.
Volume: 17
Issue: 2
Page: 1069-1083
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

Optimization techniques for siting solar-powered EV charging stations: A systematic review and methodological classification

10.11591/ijpeds.v17.i2.pp1355-1368
Linda Faridah , Rustam Asnawi , Handaru Jati , Nurwijayanti Kusuma
Solar-powered electric vehicle (EV) charging stations are essential in advancing low-carbon transportation. However, determining optimal locations remains challenging due to spatial, technical, and environmental constraints. This systematic review, conducted under the PRISMA 2020 framework, synthesizes optimization techniques for siting solar-powered EV charging stations from 15 peer-reviewed studies published between 2016 and 2024. The reviewed methods are classified into five major categories: geographic information systems (GIS)-based spatial models, multi-criteria decision-making (MCDM) frameworks, hybrid approaches integrating fuzzy logic and GIS, heuristic/metaheuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO), and artificial-intelligence-based models for predictive site selection. GIS-MCDM hybrid approaches were the most prevalent, offering improved robustness in spatial decision-making. Nevertheless, the literature reveals persistent gaps, including limited empirical validation, insufficient use of real-time data, and weak integration with smart-grid planning. This review provides a structured methodological classification, highlights sustainability considerations, and outlines a research roadmap toward intelligent, data-driven, and sustainable EV infrastructure planning aligned with global energy-transition goals.
Volume: 17
Issue: 2
Page: 1355-1368
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

A hybrid simulation and hardware approach for a regenerative braking system in an electric motorcycle

10.11591/ijpeds.v17.i2.pp1265-1278
Faris Anwar Amir Faisal , Siti Fauziah Toha , Nurul Muthmainnah Mohd Noor , Ahmad Syahrin Idris , Mohamad Osman Tokhi
Conventional electric motorcycles mostly depend on mechanical braking systems that dissipate kinetic energy as heat, resulting in significant energy losses, frequent battery recharging, and reduced operational efficiency. To address these limitations, a regenerative braking system (RBS) is designed and developed to recover and store kinetic energy during braking phases. The proposed RBS integrates a brushless DC (BLDC) motor that serves as a propulsion and energy regenerative unit, a lithium-ion battery for energy storage, and an Arduino microcontroller for real-time control and seamless system integration. A hybrid methodology combining MATLAB/Simulink simulations and hardware prototyping was adopted to evaluate system performance under various operating conditions. The simulation results demonstrated effective braking torque generation and back electromotive force (EMF) recovery to validate the system’s ability to convert kinetic energy into storable electrical energy. The proposed RBS achieved a theoretical energy recovery efficiency of approximately 70%, attributed to internal resistance and motor back EMF variations. These findings demonstrates the potential of regenerative braking in improving the energy efficiency of electric motorcycles, extending battery life, and reducing dependency on external charging. Furthermore, this study establishes a foundation for future RBS development incorporating lightweight materials, cost-effective components, and intelligent control strategies that can contribute to advancing sustainable and energy-efficient urban mobility solutions.
Volume: 17
Issue: 2
Page: 1265-1278
Publish at: 2026-06-01

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

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

Stochastic Resonance-Aided Energy Detection for RF-Powered Cognitive Radio Networks

10.12928/telkomnika.v24i3.27596
Henry Onyemauche; Nigeria Maritime University Osuagwu , Mamilus A.; University of Nigeria Ahaneku , Vincent C.; University of Nigeria Chijindu , Obinna M.; University of Nigeria Ezeja
Conventional stochastic resonance (SR) techniques often face challenges with higher-frequency signals and parameter optimization for real-time applications, as observed in practical orthogonal frequency-division multiplexing (OFDM) systems that are vulnerable to noise uncertainty (NU). In this study, we present a novel SR-aided energy detection (ED) method that incorporates multi-taper spectrum estimation technique to improve spectrum estimation precision and Gauss-Seidel-like iteration method to accurately adjust the SR parameters for real-time adaptation. This combined strategy enhances weak signal detection, prevents signal distortion, and increases robustness against fluctuating noise conditions. Results from 5,000 Monte Carlo simulations showed that, at 0 dB NU, SR-aided ED attained 90% detection probability at -11 dB, outperforming conventional ED with an SNR gain of 12.5 dB. At 3 dB NU, the conventional ED accuracy degraded by 5.5 dB, resulting in a false alarm probability of 77%, while SR-aided ED demonstrated robustness to NU. At 10 dB NU, ED failed to distinguish the differences between noise and signal power, giving rise to 99% false alarm probability. In contrast, despite a 6 dB degradation, the developed SR-aided ED approach still guarantees a 1% false alarm probability. In clipping-prone systems, conventional ED is vulnerable to signal clipping. Conversely, SR-aided ED remains unaffected.
Volume: 24
Issue: 3
Page: 786-800
Publish at: 2026-06-01

Super-twisting MPPT enhanced via grey wolf optimization for dynamic PV operation

10.11591/ijpeds.v17.i2.pp1475-1485
Slimane Hadji , Said Aissou , Abdelhakim Belkaid
This paper introduces a hybrid maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems under rapidly varying irradiance conditions. The approach combines the super-twisting algorithm (STA), a second-order sliding mode control technique, with the grey wolf optimizer (GWO) in a coordinated framework where control action and parameter adaptation are jointly addressed. Unlike conventional MPPT methods that treat control and optimization separately, the proposed scheme improves transient response while limiting steady-state oscillations. The method is evaluated through MATLAB/Simulink simulations under multiple dynamic irradiance profiles, including fast-changing environmental conditions. Performance is assessed using complementary metrics, namely tracking efficiency, convergence dynamics, and root mean square error (RMSE), to provide an objective analysis. Results show that the STA-GWO strategy achieves faster convergence and improved stability compared to conventional SMC-GWO. It reaches an average tracking efficiency of 99.34%, compared to 99.19% for SMC-GWO, with reduced power fluctuations reflected by a lower RMSE. These improvements indicate a better trade-off between dynamic performance and steady-state accuracy. While this study is based on simulations, its findings require experimental validation. Future work will therefore include real-time implementation to confirm the practical applicability of the proposed approach.
Volume: 17
Issue: 2
Page: 1475-1485
Publish at: 2026-06-01

Parameters optimization of solar PV cell using war strategy

10.11591/ijpeds.v17.i2.pp1418-1425
Radouan Gouaamar , Seddik Bri
Enhancing photovoltaic models' performance and dependability requires optimal parameter extraction. This paper presents a practical method for determining these values from experimental current-voltage data: the war strategy optimization algorithm. RTC France, PWP201, and STP6-120/36 are the three PV models to which the war strategy optimization algorithm was successfully applied. According to the findings, the RMSE values for RTC France were 0.0000077298; PWP201 was 0.0020528; and STP6-120/36 was 0.0014253. These results demonstrate the great potential of the warfare strategy optimization (WSO) to improve the accuracy of photovoltaic models and advance photovoltaic technology.
Volume: 17
Issue: 2
Page: 1418-1425
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

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

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

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
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