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Hybrid AI-driven intelligent fault diagnosis and localization in modern power systems

10.11591/ijpeds.v17.i3.pp2281-2290
Deepa Somasundaram , M. Sowmya , R. Priya , Sandip D. Satav , P. Arthi Devarani , Jayashree Kathirvel
This paper presents a hybrid intelligent framework for fault diagnosis and localization in modern power distribution systems, addressing challenges such as noisy measurements, high-impedance faults (HIF), and uncertain operating conditions. The proposed approach integrates deep neural networks (DNN) for nonlinear feature extraction, support vector machines (SVM) for robust classification, and a fuzzy inference system for uncertainty-aware decision fusion, combining the strengths of deep learning, machine learning, and soft computing. A comprehensive dataset of over 12,000 fault instances is generated using IEEE 33-bus and 69-bus systems, covering multiple fault types (LG, LL, LLG, LLL), fault resistances (0.1-200 Ω), varying load conditions, and noise levels from 30 dB to -5 dB SNR. Wavelet-based denoising and hybrid feature extraction (time–frequency and statistical features) are employed to capture transient characteristics. The DNN generates discriminative feature embeddings, which are classified using an RBF-kernel SVM and further refined through fuzzy logic with Gaussian membership functions. Fault localization is performed using impedance-based estimation enhanced by learned correction. Results show that the proposed model achieves 98.5% classification accuracy, outperforming DNN (93.2%), SVM (90.4%), random forest (91.1%), and k-NN (88.6%). The model demonstrates strong noise robustness, with only -6% accuracy degradation at -5 dB SNR. It achieves fault localization error of 0.2-0.7 km and HIF detection with F1-score of 0.91. With inference latency of 45 ms (reduced to 28 ms after optimization), the system is suitable for real-time deployment, providing a scalable and reliable solution for smart grid fault monitoring.
Volume: 17
Issue: 3
Page: 2281-2290
Publish at: 2026-09-01

Hybrid active filter control for harmonic reduction in quadratic boost converter-integrated grid systems

10.11591/ijpeds.v17.i3.pp1564-1580
S. Venkata Ramana Rao , Mahiban Lindsay
This article examines the proportional-integral (PI) and fractional-order proportional-integral-derivative (FOPID) controllers for a quasi-buck converter-hybrid active filter (QBC-HAF) to improve the harmonic suppression and power quality of a hybrid energy storage system (HESS) fed to the grid. This system uses a high-gain QBC, which is connected to a VSI to control the DC-link voltage and to allow controlled energy transfer from photovoltaic and battery sources. A MATLAB/Simulink test bed and hardware prototype were developed to validate performance. The results demonstrate the enhanced stability and quality of the output waveforms for both controllers, with the FOPID providing better harmonic suppression, reducing output current total harmonic distortion (THD) from 4.34% to 4.14% and output voltage THD from 4.75% to 4.42%. The simulation results were validated using experimental tests, showing that the FOPID-controlled QBC-HAF system is suitable for power quality improvement, voltage stabilization, and dynamic performance in renewable integrated grids.
Volume: 17
Issue: 3
Page: 1564-1580
Publish at: 2026-09-01

Energy optimization of an electric vehicle charging station using a hybrid STA-GWO MPPT strategy

10.11591/ijpeds.v17.i3.pp2183-2196
Samia Amrouni , Said Aissou , Rafik Medjoudj , Elyazid Amirouche , Nabil Benyahia , Abdelhakim Belkaid
This paper presents a hybrid electric vehicle charging station powered by both a PV source and the utility grid, incorporating an energy management strategy that prioritizes the utilization of solar energy while exporting surplus power to the grid during periods of low charging demand. To enhance the performance of maximum power point tracking, a hybrid control strategy integrating the grey wolf optimizer (GWO) and the super-twisting algorithm (STA) is proposed. The GWO performs rapid global exploration to accurately identify the maximum power point, whereas the STA ensures precise, robust, and chattering-free tracking under steady-state operating conditions. The proposed system was modeled in MATLAB/Simulink and validated under a dynamic irradiance profile characterized by both abrupt and gradual variations. Simulation results demonstrate a convergence time of 2-3 ms, residual power oscillations below 0.1%, and an average tracking efficiency of 99.38%. Compared with conventional MPPT techniques, the proposed STA-GWO approach significantly suppresses steady-state oscillations, accelerates convergence, and prevents MPP tracking failure under rapid irradiance fluctuations through the global optimization capability of GWO. These findings highlight the effectiveness of the proposed hybrid MPPT strategy in improving the robustness, energy conversion efficiency, and grid integration capability of PV-powered EV charging stations, making it a promising solution for next-generation sustainable charging infrastructure.
Volume: 17
Issue: 3
Page: 2183-2196
Publish at: 2026-09-01

Comparative simulation of fractional-order PD sliding mode and fuzzy logic controllers for a second-order discrete-time nonlinear system

10.11591/ijpeds.v17.i3.pp1822-1830
Ahmed Bennaoui , Salah Benzian , Hamza Sulimani , Aissa Ameur
Tight output regulation in power converters and electric drive systems requires a control strategy that simultaneously minimizes tracking error and maintains smooth actuation-two objectives that are intrinsically in tension for nonlinear, parameter-varying plants. Despite the widespread deployment of fractional-order PD sliding mode control (FOPD-SMC) and Mamdani fuzzy logic control (FLC) in this domain, no prior study has placed them in a direct, metric-identical comparison on a common plant. The present work closes this gap by implementing both controllers on the same second-order discrete-time nonlinear plant-representative of DC-DC converter output dynamics and motor-drive input-output behavior and evaluating them under a composite reference that combines sinusoidally-modulated ramps with step transitions, scored by the integral of squared error (ISE) and integral of absolute error (IAE). FOPD-SMC achieves ISE= 1.639 × 10-2 and IAE= 3.345 × 10-2, outperforming FLC by 87.6% and 50.4%, respectively; the advantage originates from the non-integer memory embedded in the sliding surface via the Gr¨unwald-Letnikov operator and from the explicit decomposition of the control law into nominal-tracking and robustness components. FLC, conversely, produces a chattering-free, continuously varying control signal a structural consequence of smooth Gaussian membership functions and linguistic rule aggregation, at the cost of a mean absolute tracking error twice that of FOPD-SMC. These findings establish a quantitative selection criterion: FOPD-SMC is recommended when tight voltage or current regulation is the primary objective, while FLC is preferred where smooth torque delivery and reduced actuator stress outweigh marginal gains in tracking accuracy.
Volume: 17
Issue: 3
Page: 1822-1830
Publish at: 2026-09-01

Evaluation of hybrid and standalone learning models for predicting lithium-ion battery capacity degradation

10.11591/ijpeds.v17.i3.pp1581-1590
Shobana Devendiren , A. Muthuraman , M. Vanitha , I. Arul Doss Adaikalam , R. Kalaivani , P. Kavitha
The prediction of lithium-ion battery capacity degradation plays a vital role in ensuring safe and efficient operation in electric mobility and renewable energy applications. This paper evaluates standalone machine learning, deep learning, and hybrid models for battery capacity estimation. The evaluated ML models include random forest, gradient boosting, and extreme gradient boosting (XGBoost), while the DL model employs a multilayer perceptron. The hybrid framework combines DL based feature extraction with ensemble ML regression or classification. A real-world dataset comprising temperature, resistance, reactance, and battery type was preprocessed, scaled, and divided into training and testing subsets. Hyperparameter tuning, k-fold cross-validation, and uncertainty quantification were incorporated to improve reliability and reproducibility. Model performance was assessed using RMSE, MAE, and R² for regression and receiver operating characteristic–area under the curve (ROC-AUC) and F1-score for classification. ROC curves, calibration curves, metric-comparison charts, cycle-wise degradation plots, and residual analyses were used for evaluation. Results demonstrate that the hybrid model outperforms standalone approaches by reducing RMSE and improving calibration, reliability, uncertainty alignment, and interpretability. This also establishes its novelty over existing state of health (SOH) models and highlights future extensions involving LSTM-based temporal modeling and chemistry-adaptive transfer learning. Overall, hybrid modeling provides a promising solution for reliable predictive battery maintenance.
Volume: 17
Issue: 3
Page: 1581-1590
Publish at: 2026-09-01

Optimal FOPID control for improved load frequency regulation of a renewable-integrated hybrid power system using the Puma optimizer

10.11591/ijpeds.v17.i3.pp2013-2028
Mitali Samal , Chinmoy Kumar Panigrahi , Deepak Kumar Gupta
This study addresses the challenge of frequency stabilization in a complex two-area hybrid power system subjected to load disturbances. Area 1 comprises thermal, hydro, and wind power generation, while Area 2 integrates thermal, hydro, and gas turbine units, presenting a diverse and realistic system for control strategies. The inherent nonlinearities and varying dynamics of these generation sources, particularly wind power, necessitate a robust and advanced control solution. The proposed Puma optimizer-driven fractional-order proportional-integral-derivative (FOPID) controller is developed to enhance dynamic performance under load disturbances and renewable energy fluctuations. The proposed Puma optimizer-based fractional-order proportional-integral-derivative (PO-FOPID) controller is rigorously validated against a Puma optimizer-based proportional-integral-derivative (PO-PID) controller, a PSO-tuned PID controller, and a PSO-tuned fractional-order PID (PSO-FOPID) controller. The settling time and peak overshoot/undershoot are considered for comparison, while the integral of time multiplied by the absolute error (ITAE) performance index is used as the objective function to be minimized. The findings highlight the potential of combining Puma optimizer with fractional-order control for reliable frequency regulation in modern hybrid power systems.
Volume: 17
Issue: 3
Page: 2013-2028
Publish at: 2026-09-01

FEA-based optimization of switches for reluctance motors

10.11591/ijpeds.v17.i3.pp1675-1687
Hiba Esam Aziz , Abdullah K. Shanshal , Imad Idan Abed Al-Khalaf , Tamer Kamel
Switched reluctance motors (SRMs) are considered one of the important machines used in industry sectors due to their simple structure, robustness, and high-speed capability. However, the performance is often limited by high torque ripple and acoustic noise due to the uneven magnetic flux distribution. This paper presents an optimization study for improving flux distribution by shape design modifications of the stator and rotor geometry using finite element analysis (FEA) combined with the whale optimization algorithm (WOA). More specifically, FEA is utilized to calculate the magnetic field behavior, torque characteristics, and core losses for different structural geometries of the proposed design, while WOA systematically searches for the optimum values of shape parameters. Thus, the simulation results show that the proposed approach significantly improves the uniformity of flux distribution, hence reducing torque ripple and improving the efficiency. The integration of FEA with the WOA provides a practical and effective way to achieve better SRM performance without further complication of control algorithms.
Volume: 17
Issue: 3
Page: 1675-1687
Publish at: 2026-09-01

Adaptive electromagnetic interference mitigation for wide-bandgap power converters: a review

10.11591/ijpeds.v17.i3.pp1941-1949
Md Shishir Rahman , Siti Mahfuza Saimon , Shahrin Md Ayob , Muhammad Yusof Mohd Noor
Wide-bandgap (WBG) semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) enable higher switching frequencies, greater efficiency, and increased power density, but their fast-switching transients and steep dv/dt and di/dt characteristics generate substantially elevated electromagnetic interference (EMI). This review synthesizes peer-reviewed studies to quantitatively compare EMI mitigation outcomes across source-side modulation, gate-driving, filtering, packaging, and AI-based techniques for WBG converters. Packaging-integrated common-mode screens cut CM current by up to 26 dB while raising partial-discharge inception voltage by 53%. Chaotic PWM with passive filtering reaches up to 50 dB attenuation with a 74% reduction in inductor volume. AI-based closed-loop adaptation attains up to 19.2 dB average attenuation with 98.5% CISPR 25 compliance, while RL-based filters reach 25-30 dB across wide frequency ranges. Active gate-driving reduces peak EMI by 19-39 dB. No single technique simultaneously delivers the highest suppression, efficiency, and lowest cost. Hybrid Si/WBG design currently offers the most balanced trade-off. These outcomes are consolidated into a taxonomy of propagation mechanisms, mitigation techniques, application-specific strategies across five domains, and open gaps in standardized testing and validation. These findings provide a practical, quantitative reference for engineers designing next-generation, EMC-compliant WBG power electronic systems.
Volume: 17
Issue: 3
Page: 1941-1949
Publish at: 2026-09-01

Adaptive forget-gated BiLSTM enhanced by DTW based feature selection in solar PV forecasting

10.11591/ijpeds.v17.i3.pp2086-2100
Are Sambasiva Rao , Kunada Dhana Sree Devi
Growing exhaustion of fuel reserves and their harmful environmental impacts have driven the shift towards the maintenance of renewable energy sources like solar energy. PV systems, however, still face significant challenges while integrating renewable sources into existing utility systems. Particularly, environmental features like temperature, irradiance, and humidity are not perfectly well coordinated with the power output, are always asynchronous in nature, and affect the power prediction considerably. Close observation of energy datasets from many PV plants revealed many intrinsic environmental variables that are highly asynchronous. Many forecasting models learn redundant features which might seem useful, and thereby the test performance is overfitting. To address the nonlinear and asynchronous behavior of environmental variables, there is a serious requirement for intelligent feature selection algorithms guided by both correlation and temporal alignment metrics. This research proposes a novel adaptive dynamic time warping (A-DWT) feature selection with an adaptive forget gate (AFG-BiLSTM) to address the asynchronous issues. Experiments were conducted with varied environmental asynchronous features, and the results of the proposed model were compared with traditional BiLSTM and stacked BiLSTM models. In all the experiments, the proposed model showed decreased error by (94.9%) on Dataset-1 (0.069, 0.0035), by (94.6%) on Dataset-2 (0.078, 0.0042), and by (90.9%) on Dataset-3 (0.069, 0.0061) when compared to stacked BiLSTM. The MSE loss of the proposed method was observed to be between (0.3%) and (11%) on four datasets.
Volume: 17
Issue: 3
Page: 2086-2100
Publish at: 2026-09-01

Optocoupler-based closed-loop hysteresis control for buck converter: experimental results

10.11591/ijpeds.v17.i3.pp1842-1851
David Estrada Grisales , José Ángel Buitrago Gutiérrez , Fredy E. Hoyos
This work experimentally evaluates a simplified closed-loop on-off (hysteresis) controller for a single-phase buck converter using an LM324 comparator and an optocoupler-based gate drive to the IRF830 MOSFET. The controller compares the output voltage against a reference and drives the switch in a binary manner, yielding variable-frequency regulation with low component count. Performance is assessed for DC-DC regulation and dynamic reference tracking under sinusoidal, triangular, and rectangular waveforms at 1 Hz and 60 Hz, as well as under input-voltage droop, load perturbations, and real-time changes in reference waveform. Results show envelope-accurate tracking with fast transients and bounded ripple across all tested conditions, including representative grid-frequency operation. The controller maintains stability and preserves waveform fidelity despite supply and load disturbances; edge transients observed for rectangular references at 60 Hz are consistent with expected output-capacitor dynamics and remain bounded. The findings support the suitability of analogue on-off hysteresis with optocoupler isolation as a low-complexity approach for applications requiring efficient DC-DC conversion and dynamic signal tracking, especially where cost, simplicity, and rapid response are prioritized.
Volume: 17
Issue: 3
Page: 1842-1851
Publish at: 2026-09-01

Accurate RUL prediction of EV batteries using random forest and ensemble learning frameworks

10.11591/ijpeds.v17.i3.pp2291-2300
Ponkumar Ganesapandiyan , P. Hemachandu , N. Rajavinu , M. Bhoopathi , P. Kavitha , R. Kalaivani
Precise remaining useful life (RUL) estimation for lithium-ion batteries is essential for improving the safety, reliability, and maintenance of electric vehicles (EVs). This study proposes a random forest (RF)-based ensemble learning framework using the publicly available Hawaii Natural Energy Institute (HNEI) dataset containing 15,064 charge-discharge cycles. Seven degradation-related features, including cycle index, discharge time, voltage decrement, maximum discharge voltage, minimum charging voltage, time at 4.15 V, and constant-current charging duration, are extracted to characterize battery aging. The proposed RF model is compared with linear regression (LR), long short-term memory (LSTM), and attention-LSTM models using MAE, root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²). RF demonstrates superior prediction performance, achieving MAE of 5.20, RMSE of 6.83, MAPE of 1.33%, and R² of 0.997. Parity and residual analyses further confirm its strong predictive consistency. The proposed approach provides an accurate, computationally efficient, and interpretable solution for BMS applications, enabling effective battery health monitoring, predictive maintenance, charging optimization, and timely replacement.
Volume: 17
Issue: 3
Page: 2291-2300
Publish at: 2026-09-01

Design of PI and fuzzy-based sliding mode controller for speed control of BLDC motor using Landsman converter in electric vehicles

10.11591/ijpeds.v17.i3.pp1873-1884
S. Sahana , K. Sasikala
This research paper compares a conventional proportional-integral (PI) controller with a fuzzy-based sliding mode controller (FSMC) for speed control of a brushless DC (BLDC) motor powered by a Landsman DC-DC converter for electric vehicle (EV) applications. MATLAB/Simulink is used to model and simulate the proposed system's cascaded control architecture, which consists of a voltage source inverter and a Landsman converter. The performance is assessed in steady-state, load-disturbance, and transient scenarios. According to simulation data, the FSMC outperforms the PI controller in terms of rise time (0.064 s), overshoot (0.17%), and settling time (0.212 s). The FSMC exhibits better resilience, limiting the speed reduction to 45 RPM with a recovery time of 0.04 s with a sudden load disturbance of 1 Nm. The findings verify that the FSMC is appropriate for high-performance BLDC motor drive systems in electric vehicle applications because it provides improved dynamic response and disturbance rejection.
Volume: 17
Issue: 3
Page: 1873-1884
Publish at: 2026-09-01

Design and analysis of modified single switch AC-DC bridgeless Cuk converter

10.11591/ijpeds.v17.i3.pp1902-1913
Abdullah Sahib Abdulsada , Hassan Wahhab Salih , Nasir Hussein Selman
The Cuk converter is widely used in power electronics due to its ability to provide both step-up and step-down voltage conversion with low ripple characteristics. However, conventional designs suffer from increased component count, higher losses, and significant voltage stress on switching devices. In this paper, an improved single-switch bridgeless AC-DC Ćuk converter is proposed for power factor correction (PFC) and loss reduction. The proposed topology employs a two-stage integrated structure with two low-value coupling capacitors (1 µF each). These capacitors effectively reduce voltage stress across the main switch and enhance switching performance. The operating principles of the proposed converter are analyzed under different switching conditions, highlighting its improved dynamic behavior and reduced component stress. Simulations using MATLAB/Simulink show that the proposed design achieves high efficiency, very low output voltage ripple (less than 1%), and a near-unity power factor of 0.9988. This performance, particularly the fast dynamic response, was achieved using a specially designed proportional-integral (PI) controller for output voltage regulation. The converter demonstrates fast dynamic response with a settling time of less than 0.23 s and a maximum overshoot of 7.5% under sudden load and input variations. Compared to conventional two-switch topologies, the proposed design significantly reduces switch voltage stress while maintaining a simpler structure, leading to lower cost and compact size. These results confirm that the proposed converter is a promising solution for high power quality applications.
Volume: 17
Issue: 3
Page: 1902-1913
Publish at: 2026-09-01

DPC backstepping control applied to an active rectifier: analysis and comparison with a PIL implementation

10.11591/ijpeds.v17.i3.pp1808-1821
Slimane Chiboub , Mohamed Khafallah , Jawad Lamterkati
This paper presents a simulation and comparison of direct power control (DPC) and the new backstepping (BS) control for a three-phase PWM rectifier. The objective is to evaluate these techniques in achieving high-performance grid integration, targeting the absorption of sinusoidal current, maintaining a unity power factor, and minimizing grid current harmonics. While DPC is recognized for its fast dynamic response, it often suffers from high power ripples and variable switching frequency due to the switching table. Conversely, the proposed BS control provides superior tracking performance and improved stability margin, even under parameter uncertainties. Through detailed modeling and simulation in MATLAB/ Simulink environment, this paper analyzes key performance indicators, including current total harmonic distortion (THD), response time, and robustness, under multiple grid conditions, and validates the proposed control law using a processor-in-the-loop (PIL) implementation. The comparative study demonstrates the trade-offs between the two control methods, highlighting the advantage of backstepping control.
Volume: 17
Issue: 3
Page: 1808-1821
Publish at: 2026-09-01

Adaptive internal model control-proportional integral for robust control of three-phase active front-end rectifiers

10.11591/ijpeds.v17.i3.pp1794-1807
Azizah Abdul Razak , Norjulia Mohamad Nordin , Razman Ayop , Hazlina Selamat , Abobaker Kikki Abobaker , Nik Rumzi Nik Idris , Tole Sutikno
Three-phase active front-end (AFE) rectifiers are widely deployed in motor drives, electric vehicle chargers, and grid-connected renewable energy systems, where precise DC-link voltage regulation is essential for stable converter operation. In practice, DC-link capacitance degrades over time, and load profiles vary dynamically, both degrading the DC-link voltage regulation performance. Conventional proportional-integral (PI) outer voltage controllers are designed based on nominal operating conditions, with limited stability margins resulting in sluggish or oscillatory DC-link voltage responses under significant load and parameter variations. This paper proposes an adaptive internal model control-proportional integral (AIMC-PI) outer voltage loop controller for a three-phase AFE rectifier. It extends the conventional IMC-PI structure by incorporating an active damping term, an internal feedforward gain, a reference filter, and a Lyapunov-based adaptation law that updates the embedded plant model and IMC filter time constant online ensuring closed-loop stability and bounded tracking error. Simulation results show that AIMC-PI achieves a faster dynamic response than PI and performance comparable to IMC-PI and linear active disturbance rejection control (LADRC) under nominal conditions. As DC-link capacitance degrades to 0.5C, AIMC-PI maintains a well-damped DC-link voltage, whereas LADRC exhibits noticeable oscillations. Experimentally, AIMC-PI successfully eliminates the AC-supply current and DC-link voltage ripples present in fixed-λ IMC-PI.
Volume: 17
Issue: 3
Page: 1794-1807
Publish at: 2026-09-01
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