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31,456 Article Results

Reliability-constrained optimal scheduling of PV-based microgrids using deterministic time-series forecasting and load prioritization strategies

10.11591/ijpeds.v17.i1.pp250-266
Dunya Sh. Wais , Huda A. Abbood
This paper presents an advanced MPC-based energy scheduling framework for islanded microgrids operating under uncertain and dynamic conditions where photovoltaic (PV) generation and energy storage systems (ESS) are integrated, and load management is hierarchically prioritized. The framework employs a hybrid ARIMA and random forest forecasting model to improve day-ahead and intra-day predictions of PV generation and load demand, enabling intelligent demand response, prioritized load shedding, and adaptive storage operation. Moreover, the proposed framework incorporates time-of-use (TOU) pricing and load importance weighting to minimize operational costs while ensuring a reliable power supply for critical loads. Simulation results across four operational scenarios demonstrate that the proposed method achieves approximately 32% improvement in critical load protection, 30% reduction in total operating cost, and 33.3% decrease in total load shedding compared to conventional MPC-based approaches. The proposed approach, therefore, provides a comprehensive, dynamic, and cost-efficient solution for microgrid scheduling and can be extended to multi-microgrid cluster applications in future research.
Volume: 17
Issue: 1
Page: 250-266
Publish at: 2026-03-01

High efficient DC-AC inverter for low wireless power transfer applications

10.11591/ijpeds.v17.i1.pp453-464
Kyrillos K. Selim , Hanem Saied Ebrahem Torad , Mostafa R. A. Eltokhy , Hesham F. A. Hamed , Mohamed Elzalik
The inverter's simplicity is an important aspect that must be considered especially for electronic devices, as adding the number of power switches increases the complexity and overall cost of the inverter. This work proposes an inverter design that converts DC into AC power. It receives 12 VDC as an input voltage, and it is composed of a boost converter that converts an input voltage of 5-20 VDC to an output voltage of 4-30 VDC and a pulse width modulation controller to produce a square wave with a frequency of 100 kHz to drive the switching MOSFET. The designed inverter can be operated on different loads ranging from 50 Ω to 1000 Ω, tested in both simulations and experimentally. The design was optimized by the LT Spice simulator. The proposed inverter has operating frequencies ranging from 40 kHz to 110 kHz, taking into account different loads. The obtained results showed that both simulation and experimental results converged, whereas the highest efficiency was 96.96% at 55 kHz at a fixed load of 100 Ω. On the other hand, the maximum achieved efficiency when the load was sweeping was 80% at a load of 50 Ω at a fixed frequency of 100 kHz.
Volume: 17
Issue: 1
Page: 453-464
Publish at: 2026-03-01

THD and spectral performance analysis of two-triangle RPWM for inverter applications

10.11591/ijpeds.v17.i1.pp370-382
G. Jegadeeswari , R. Sundar , S. P. Manikandan , E. Poovannan , C. Rajarajachozhan , M. Batumalay , Sukumar Kalpana
Pulse width modulation (PWM) is essential for voltage source inverters (VSI) to generate high-quality voltage outputs. Conventional deterministic PWM generates predictable harmonics, causing clusters that increase acoustic noise. Random PWM (RPWM) disperses harmonic power over a wider frequency range, reducing noise and electromagnetic interference. Many RPWM techniques improve inverter quality, but only partially suppress dominant harmonics and lack effective harmonic spreading. Most studies focus on simulations with limited FPGA implementation or hardware validation. The use of digital tools like VHDL, ModelSim, and MATLAB co-simulation remains underutilized. This paper proposes two-triangle RPWM strategies to enhance harmonic dispersion and reduce total harmonic distortion (THD). Co-simulation results are shown for both SPWM and RPWM, along with comparisons of fundamental voltages, THD, and HSF across different modulation indexes. Additionally, synthesis data for the Xilinx XC3S500E FPGA processor is supplied. The last section offers a comparative analysis and experimental validation of SPWM and RPWM. These techniques enable enhanced inverter performance, lower acoustic noise, and process innovations in power electronic systems.
Volume: 17
Issue: 1
Page: 370-382
Publish at: 2026-03-01

Investigation of efficiency and safety in wireless capacitive power transfer through a single-layer tissue phantom

10.11591/ijpeds.v17.i1.pp502-517
Yusmarnita Yusop , Amy Sarah Ngu , Cheok Yan Qi , N. B. Asan , Huzaimah Husin , Shakir Saat , Peter Adam Hoeher
Wireless power transfer (WPT) is a promising solution for implantable biomedical devices, offering an alternative to traditional implanted batteries and percutaneous connections, which are limited by short lifespans and high infection risks. Existing capacitive power transfer (CPT) systems for biomedical implants often utilize media such as animal meat or liquids to validate power transfer across the human body, but these materials exhibit inconsistent and inaccurate dielectric properties. To address this limitation, this study proposes a CPT system designed to operate with a single-layer tissue phantom that closely mimics the dielectric characteristics of human tissue. The system is integrated with a class-E LCCL resonant topology to enhance power transfer efficiency. In addition to evaluating performance, this work also investigates safety aspects in terms of electric field emission and specific absorption rate (SAR). Simulations using MATLAB Simulink and ANSYS HFSS reveal that at a 1 mm tissue gap, the electric field reaches 298.09 V/m and the SAR is 1.14 W/kg, which are both within established safety limits (614 V/m and 2 W/kg per 10 g of tissue). Furthermore, a 5 W, 1 MHz system operating across a 2 mm tissue gap demonstrates power transfer efficiencies of 40.61% for skin tissue and 20.53% for muscle tissue. These results validate the system’s safety and efficiency for powering deeply implanted biomedical devices.
Volume: 17
Issue: 1
Page: 502-517
Publish at: 2026-03-01

Application of machine learning for production optimization and predictive maintenance in an iron processing plant

10.11591/ijpeds.v17.i1.pp765-776
Lakhdari Lahcen , Mohamed Habbab , Alhachemi Moulay Abdellah
The modern metallurgical industry requires advanced solutions for process optimization, cost reduction, and predictive maintenance. This paper proposes a unified simulation-based framework using machine learning (ML) to jointly address production optimization and maintenance prediction in a virtual iron processing environment. Several ML models, including random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector machine (SVM), and k-nearest neighbors (k-NN), were evaluated on synthetic datasets representing production, maintenance, and transport processes. A reproducible methodology was adopted, including preprocessing, time-aware data splitting, and cross-validation to prevent information leakage. Model performance was assessed using F1-score, area under the receiver operating characteristic curve (AUC), and regression metrics. Tree-based models achieved near-perfect classification performance (AUC ≈ 1, precision and recall > 0.99), while light gradient boosting machine (LightGBM) and CatBoost provided the best regression accuracy. Feature importance analysis using SHapley Additive exPlanations (SHAP) identified vibration and temperature as key maintenance indicators. Although based on simulation, the framework is designed for integration with supervisory control and data acquisition (SCADA) and the Industrial Internet of Things (IIoT), supporting real-time industrial deployment and alignment with operational key performance indicators.
Volume: 17
Issue: 1
Page: 765-776
Publish at: 2026-03-01

A novel approach to flexible BTMS for 2-wheeler electric vehicle to avoid fire accidents-an Indian perspective

10.11591/ijpeds.v17.i1.pp49-57
Mahmooda Mubeen , Gangishetti Srinivas
The Indian electric vehicles market share has significantly increased due to various government initiatives, increased fuel prices, and charging infrastructure. On the contrary, fire accidents of EV’s in India are no rarer due to inappropriate BTMS and its inability to work with different environmental conditions prevailing in India, so it has become one of the major concerns. Two-wheelers, being one of the most used modes of transport, are dominating the Indian roads; it well deserves an innovative BTMS that suits local environmental conditions for preventing thermal runaways and maintaining better performance of the battery. As we get to see diverse environmental conditions at different parts of India, it will be good if we can develop flexible BTMS. Major challenges being faced in the development of suitable BTMS are space and cost constraints. This paper focuses on the development of BTMS for electric two-wheelers, suitable for various environmental conditions, which fits in the available space with low additional cost. It also provides flexibility to drop or add some of the features based on one’s operational requirements or environmental conditions prevailing at the place of operation, which can be as easy as one can drop or choosing to have fog lamps, speakers, camera, and sunroof depending upon their requirement and budget.
Volume: 17
Issue: 1
Page: 49-57
Publish at: 2026-03-01

Performance evaluation of cascaded H-bridge multilevel inverter with hybrid controller based PV system

10.11591/ijpeds.v17.i1.pp37-48
C. Dinakaran , T. Padmavathi
Rising concerns about global warming demand renewable growth, which in turn needs efficient converter topologies to integrate renewable power. This article presents a single-phase, nine-level inverter to improve the performance of non-conventional power systems. Here, the foremost aim, based on the advanced techniques, to diminish the representation of switches with sources has been executed. This influences the appended preservation of generating energy against non-conventional power resources. This conquest during the statistic of switch refuses every switching loss, counting the cardinal-like driving circuit that details a minimization within convolution based on supervision track, consequently depreciating the disturbances with scope. The proposed inverter has a diminished production voltage total harmonic distortion (THD) with an ideal power factor. The cascaded H-bridge multilevel inverter (CHBMLI) topology is intended for the proposed method in support of the design, added ant-lion optimization (ALO) tuned fuzzy logic controller (FLC) methodical assessment for compensation. The presented arrangement is refined to diminish the energy losses, just as it is unified among reproducing systems that boost the smooth output voltage with reduced %THD. In addition, contraction in energy losses and amplification in efficiency are accomplished by producing transitional levels for the level elaboration system. Indeed, every completion related to the suggested arrangement is evaluated over the reproduction of MATLAB/Simulink and PROTEUS applications.
Volume: 17
Issue: 1
Page: 37-48
Publish at: 2026-03-01

Hybrid renewable energy for cold chain in Indonesia: technical and economic evaluation

10.11591/ijpeds.v17.i1.pp674-682
I Made Aditya Nugraha , I Gusti Made Ngurah Desnanjaya , Anis Khairunnisa , Mahaldika Cesrany
Cold storage plays a crucial role in preserving temperature-sensitive products, particularly in the fisheries and food sectors. However, its operation is highly energy-intensive and often constrained by unstable electricity supply in many Indonesian regions. This study quantitatively evaluates a hybrid renewable energy system integrating photovoltaic (PV) panels, diesel generators, batteries, and the utility grid to ensure sustainable cold storage operations. Using measured load profiles, solar irradiation data, and annual operating costs, the system achieved a 60% reduction in diesel fuel consumption, 30-50% lower CO₂ emissions, and annual savings exceeding IDR 100 million compared to conventional generator-based systems. The system demonstrated 83.5% overall efficiency, with a payback period of 4.4 years and a positive net present value (NPV), confirming its economic viability. The novelty of this research lies in presenting the first comprehensive techno-economic analysis of a PV-diesel-battery-grid hybrid system specifically designed for fisheries-based cold storage facilities in Indonesia, considering local solar potential and grid reliability. Despite its feasibility, implementation challenges remain, including a lack of skilled technicians, limited financial incentives, and bureaucratic constraints. To overcome this, the study recommends PV subsidies, low-interest green loans, and public–private partnerships aligned with Indonesia's energy transition roadmap and cold chain development goals.
Volume: 17
Issue: 1
Page: 674-682
Publish at: 2026-03-01

Mitigating harmonic distortion in grid-connected PV systems: a comparative evaluation of ANFIS and IC-based MPPT techniques

10.11591/ijpeds.v17.i1.pp303-316
Bouledroua Adel , Mesbah Tarek , Kelaiaia Samia
Integrating photovoltaic (PV) systems into power grids presents notable challenges related to power quality, especially concerning total harmonic distortion (THD) caused by power electronic converters, which do not comply with IEEE 519 and IEC 61000 standards. This study introduces an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) controller designed to optimize energy extraction while concurrently mitigating harmonic distortion in three-phase grid-connected PV systems. Unlike conventional IC-MPPT methods, which compromise the quality of the power to monitor performance, the proposed ANFIS-MPPT strategy uses intelligent modulation index adjustment to achieve a dual goal. A comparison of the four irradiation levels (450-1000 W/m²) shows a higher performance: ANFIS-MPPT achieves 0.26% THD at 1000 W/m² compared to 0.44% at IC-MPPT (40.9% improvement), and 0.80% versus 1.12% at 450 W/m² (28.6% reduction). The five-layer ANFIS architecture, trained on temperature and radiation data, shows faster convergence and lower oscillation than the conventional approach. The results confirm that MPPT based on ANFIS is an effective solution to improve the energy quality of grid-integrated photovoltaic installations while maintaining optimal energy conversion efficiency.
Volume: 17
Issue: 1
Page: 303-316
Publish at: 2026-03-01

Study of neural controller based MPPT in comparison with P&O for PV systems

10.11591/ijpeds.v17.i1.pp797-808
Djaafar Toumi , Mourad Tiar , Abir Boucetta , Ikram Boucetta , Ahmed Ibrahim
This study investigated the performance of two prominent maximum power point tracking (MPPT) strategies: the established perturb and observe (P&O) technique and an artificial neural network (ANN)-based controller. Through simulations conducted in MATLAB/Simulink, a 50 W photovoltaic (PV) array was evaluated under dynamic irradiance and temperature variations. Notably, data generated by the P&O system served as the training dataset for the ANN model. The simulation results indicate that the ANN controller effectively and accurately identifies the PV system’s optimal operating point even amidst fluctuating environmental conditions. When compared to the conventional P&O method, the ANN approach demonstrated superior characteristics, including a significantly faster response, diminished oscillations around the maximum power point, and enhanced tracking accuracy during rapid environmental shifts. These findings underscore the substantial potential of ANN-based MPPT strategies for improving both the efficiency and operational stability of photovoltaic power systems.
Volume: 17
Issue: 1
Page: 797-808
Publish at: 2026-03-01

Voltage compensation using fuel cell fed dynamic voltage restorer

10.11591/ijpeds.v17.i1.pp663-673
Ryma Berbaoui , Rachid Dehini
One of the basic tasks of the dynamic voltage restorer (DVR) is to maintain voltage stability in distribution systems by correcting any deviations or disturbances in the three-phase supply. Whether they are increases or decreases. However, one of its disadvantages is its power source, as it cannot supply itself with power from the electrical grid like parallel compensators, which obtain power directly from the grid. This article presents an energy study of a dynamic voltage regulator (DVR) when operated using a power source represented by fuel cells, which are considered a clean and renewable source. On the other hand, excess energy from the regenerator or fuel cells can be output and injected into the distribution network for utilization via a parallel compensator (CP). The parallel compensator also compensates for reactive energy on the reactive load side to increase the power factor measured at the source side of the distribution system. This integrated system also uses neural networks to identify voltage disturbances and determine the voltages (modules/arguments) that must be added to the voltages in the power grid for correction. This analytical study was completed using a simulation system to confirm the effectiveness of this integrated system. The distinctive feature of this study is the integration of fuel cells and neural network-based control in the DVR system, providing a sustainable and intelligent alternative to conventional configurations, which makes it different from traditional DVRs that operate with batteries and supercapacitors. Its efficiency in compensating for voltage drops and surges is evident, and it also improves the power factor and ensures reliable operation of voltage-sensitive devices.
Volume: 17
Issue: 1
Page: 663-673
Publish at: 2026-03-01

Enhancing the dynamic stability of electric power systems through the coordinated tuning of generator predictive controllers

10.11591/ijpeds.v17.i1.pp211-222
Hristo Beloev , Yuri Bulatov , Andrey Kryukov , Konstantin Suslov , Yuliya Valeeva , Magdalena Dudek , Iliya Iliev
The paper presents a method for the coordinated tuning of automatic voltage regulation (AVR) and automatic speed control (ASC) systems for a group of generators operating in parallel at a power plant. The method also involves solving the optimization problem using a genetic algorithm. The possibilities of using lead-lag elements in AVR and ASC, which impart predictive properties and improve damping characteristics of the controllers, are also considered. A model of a power plant operating in parallel with an electric power system is presented. This model demonstrates effective damping of oscillations under large disturbances when the proposed method is used to adjust the AVR and ASC control coefficients, along with a self-tuning lead-lag element. In this case, voltage oscillations and frequency overshoot disappear, and there is a significant reduction in the maximum deviations of these parameters. In the illustrative case study, the coordinated tuning of the controllers provides a 6% increase in the transmitted power limit and, as a consequence, the enhancement of the stability margin of the electric power system.
Volume: 17
Issue: 1
Page: 211-222
Publish at: 2026-03-01

Solar power forecasting using a SARIMA approach for Indonesia's grid integration

10.11591/ijpeds.v17.i1.pp293-302
Ricky Maulana , Syafii Syafii , Aulia Aulia
Indonesia’s transition toward a renewable energy-dominated power grid is progressing to meet increasing energy demands while reducing dependence on fossil fuels. According to the National Energy General Plan, their goal is to have 23% of the energy mix come from renewables by 2025 and 31% by 2050. Accurate forecasting of photovoltaic (PV) power output is crucial to address the intermittent nature of solar energy and ensure grid stability. A seasonal autoregressive integrated moving average (SARIMA) model was developed to estimate day-ahead photovoltaic power output in Padang City, Indonesia. Using NASA solar irradiance data from March 1-31, 2024, the SARIMA(1,0,1)(4,0,3)24 model achieved high accuracy with an NRMSE of 4.19%. To evaluate its performance, a comparative evaluation was conducted between the SARIMA model and two machine learning methods, namely artificial neural network (ANN) and long short-term memory (LSTM), in which SARIMA achieved the lowest forecasting error. These findings indicate that SARIMA remains an effective and interpretable statistical method for short-term PV forecasting, supporting reliable energy planning and power grid operations towards Indonesia's renewable energy goals.
Volume: 17
Issue: 1
Page: 293-302
Publish at: 2026-03-01

D-STATCOM control for distribution grids with distributed sources based on MMC structure using FCS-MPC algorithm

10.11591/ijpeds.v17.i1.pp425-437
Pham Viet Phuong , Le Hoai Nam , Pham Chi Hieu , Tran Hung Cuong
This paper proposes a D-STATCOM structure based on a modular multilevel converter (MMC) with the use of FCS-MPC control method for the purpose of compensating reactive power and stabilizing voltage in the distribution grid. The D-STATCOM is effectively used in cases involving non-sinusoidal and unstable voltages, which often occur in the distribution grid due to the effects of unbalanced nonlinear loads and power injection from renewable energy systems. The proposed structure also has the capability of reactive power compensating flexibility in fault conditions to stabilize the grid voltage. In this paper, a new control strategy, which is based on the combination of an outer PI controller and an inner FCS-MPC controller, was introduced. The outer PI controller is used to reduce static deviations in control values and to provide a reference value for the FCS-MPC controller. The inner FCS-MPC controller calculates the optimal switching state for the purpose of reducing the switching frequency of the MMC. The implementation process begins with the construction of a mathematical model and a control model. Simulations were carried out by MATLAB/Simulink to demonstrate the responsiveness of the control algorithm and the performance of D-STATCOM under the conditions of non-sinusoidal and unstable voltages.
Volume: 17
Issue: 1
Page: 425-437
Publish at: 2026-03-01

A novel hybrid PI and adaptive super-twisting sliding mode controller for high-performance integrated speed and flux regulation of IMDs

10.11591/ijpeds.v17.i1.pp414-424
Duc Thuan Le , Ngoc Thuy Pham
This paper presents a novel hybrid control strategy that integrates a proportional-integral (PI) regulator with an adaptive super-twisting sliding mode controller (ASTA) defined on a nonsingular terminal sliding mode control (NTSMC) surface for high-performance induction motor drives (IMDs). This enhanced hybrid PI-ASTA-NTSMC architecture jointly exploits the steady-state accuracy of PI control and the finite-time robustness of a higher-order sliding mode formulation. The adaptive mechanism of the super-twisting algorithm dynamically adjusts the switching gains according to the instantaneous sliding variable, ensuring consistent performance under time-varying loads and parameter variations. The NTSMC surface guarantees singularity-free finite-time convergence, while the adaptive ASTA law suppresses chattering and enhances disturbance rejection. Simulation results across multiple operating conditions show that the proposed controller significantly outperforms PI and PI-FOSMC schemes. It achieves the fastest transient, reducing settling time to 0.0407 s (39.4% and 31.5% faster than PI and PI-FOSMC), with overshoot lowered to 0.0091 rad/s and ISE/IAE minimized to 0.0035 and 0.0256, confirming its superior tracking precision. Additionally, reductions in the speed and torque RMSE indicate smoother control effort and improved closed-loop performance. The Lyapunov-based analysis confirms global finite-time stability of the overall system. With its enhanced robustness, low sensitivity to sampling noise, and continuous higher-order sliding structure that suppresses chattering, the proposed hybrid PI–ASTA–NTSMC offers a computationally efficient and practically attractive solution for integrated speed–flux control in industrial IM drives.
Volume: 17
Issue: 1
Page: 414-424
Publish at: 2026-03-01
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