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30,376 Article Results

Comparative analysis of optimization techniques for optimal EV charging station placement

10.11591/ijpeds.v16.i4.pp2860-2867
Deepa Somasundaram , G. Prakash , N. Rajavinu , D. Lakshmi , P. Kavitha , V. Devaraj
The optimal placement of electric vehicle (EV) charging stations plays a crucial role in improving accessibility, reducing travel distances, and minimizing infrastructure costs in smart urban planning. This study presents a comparative analysis of traditional optimization techniques-such as linear programming (LP), particle swarm optimization (PSO), k-means clustering, and greedy heuristic methods-alongside a machine learning-based approach using genetic algorithms (GA). A machine learning framework is implemented to simulate EV charging demand, optimize station deployment, and incorporate real-world constraints like cost, grid capacity, and user travel penalties. The results demonstrate that GA achieves superior performance in balancing cost-efficiency and user convenience, outperforming traditional techniques in solution quality under dynamic demand conditions. PSO and LP provide faster convergence but are less adaptive to changing parameters. The study highlights the potential of integrating machine learning into infrastructure planning and provides actionable insights for urban planners and policymakers in developing resilient and intelligent EV charging networks.
Volume: 16
Issue: 4
Page: 2860-2867
Publish at: 2025-12-01

Backstepping control in speed loop combined with load torque observer-ESO for IPMSM in electric vehicle

10.11591/ijpeds.v16.i4.pp2271-2279
An Thi Hoai Thu Anh , Tran Hung Cuong , Nguyen Van Hoa
Electric vehicles are gaining popularity due to their environmental friendliness and the need to conserve dwindling fossil fuel resources. In this field, interior permanent magnet (IPM) motors are considered the top choice for propulsion systems due to their high efficiency, high torque-to-current ratio, durability, and low noise. To optimize the speed control performance of IPM motors in the presence of disturbances, a nonlinear speed control algorithm for IPM systems using the backstepping method is developed in this paper. Additionally, a load torque observer using the extended state observer (ESO) method is implemented to enable the system to respond quickly and accurately to load changes while minimizing the effects of disturbances, thereby enhancing the operation and reliability of electric vehicles. The simulation results, conducted in MATLAB/Simulink, demonstrate that the combination of backstepping control and ESO offers good stability for the motor system, while mitigating the impact of disturbances and load variations. This is an important step in optimizing the control system of electric vehicles, contributing to the improvement of performance and reliability in electric vehicle applications.
Volume: 16
Issue: 4
Page: 2271-2279
Publish at: 2025-12-01

Enhanced integration of renewable energy and smart grid efficiency with data-driven solar forecasting employing PCA and machine learning

10.11591/ijpeds.v16.i4.pp2645-2654
Jayashree Kathirvel , Pushpa Sreenivasan , M. Vanitha , Soni Mohammed , T. Sathish Kumar , I. Arul Doss Adaikalam
A significant obstacle to preserving grid stability and incorporating renewable energy into smart grids is variations in solar irradiation. To improve solar power management's dependability, this research proposes a short-term solar forecasting framework powered by AI. Multiple machine learning models, such as long short-term memory (LSTM), random forest (RF), gradient boosting (GB), AdaBoost, neural networks (NN), K-Nearest neighbor (KNN), and linear regression (LR), are integrated into the suggested system, which also uses principal component analysis (PCA) for dimensionality reduction. The Abiod Sid Cheikh station in Algeria (2019-2021) provided real-world data for the model's validation. With a two-hour-ahead RMSE of 0.557 kW/m², AdaBoost had the most accuracy, whereas LR had the lowest, at 0.510 kW/m². In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. These findings highlight the efficiency of hybrid AI models based on PCA for accurate forecasting, which is crucial for smart grid stability.
Volume: 16
Issue: 4
Page: 2645-2654
Publish at: 2025-12-01

Dynamic modelling and small-signal analysis of an efficient bridge-type multi-input DC converter for hybrid low-power systems

10.11591/ijpeds.v16.i4.pp2441-2452
Baya Reddy Lomada , Vangala Naga Bhaskar Reddy
This paper presents the dynamic modelling and small-signal analysis of a bridge-type multi-input DC converter designed for hybrid low-power systems. The converter architecture supports the integration of multiple energy sources such as fuel cells and photovoltaic (PV) arrays, enabling enhanced flexibility and reliability. A CUK-based configuration is employed to achieve continuous input current and reduced voltage stress across the switches. The dynamic behavior of the converter is analyzed through average large-signal and small-signal state-space modelling. Stability is assessed using the Routh-Hurwitz criterion, and steady-state analysis is carried out to support performance evaluation. Experimental results obtained from a 250 W prototype confirm the validity of the developed models and demonstrate the efficiency and suitability of the proposed converter for hybrid renewable energy systems.
Volume: 16
Issue: 4
Page: 2441-2452
Publish at: 2025-12-01

Development of a PEM fuel cell equivalent circuit model with PINN-based parameter identification

10.11591/ijpeds.v16.i4.pp2804-2818
Ismail Ait Taleb , Zakaria Kourab , Souad Tayane , Mohamed Ennaji , Jaafar Gaber
This paper presents a novel equivalent electrical circuit model for proton exchange membrane fuel cells (PEMFCs) and introduces a physics-informed neural network (PINN) algorithm for parameter identification. The proposed model provides a more accurate representation of the fuel cell’s dynamic behavior while maintaining computational efficiency. Unlike conventional methods, the PINN framework integrates physical constraints with data-driven learning, ensuring physically consistent parameter estimation. To validate its effectiveness, the proposed model is compared with the widely used RC equivalent circuit and a generic PEMFC model. Experimental data from a 1.2 kW PEMFC test bench serve as a benchmark for evaluating the transient and steady-state performance of each modeling approach. Results demonstrate that the proposed circuit, combined with PINN-based identification, yields enhanced accuracy in predicting voltage response under various operating conditions. Additionally, the model exhibits improved adaptability to transient phenomena compared to conventional equivalent circuits. These findings highlight the potential of physics-informed machine learning for advancing fuel cell modeling and control strategies.
Volume: 16
Issue: 4
Page: 2804-2818
Publish at: 2025-12-01

Speed control of 3-phase induction motor with modified DTC using HTAF-ANN

10.11591/ijpeds.v16.i4.pp2197-2211
Arpita Banik , Raja Gandhi , Chandan Kumar , Achyuta Nand Mishra , Rakesh Roy
In this research paper, an artificial neural network (ANN) algorithm is implemented with modifications to enhance the performance of a direct torque controlled (DTC) induction motor drive. Since the main challenge in the conventional DTC technique is to tune the PI controller appropriately therefore in this work, an ANN technique is incorporated in place of the conventional PI controller. Sudden changes in speed and loading in induction motor drives lead to sharp fluctuations and disturb the motor performance. In order to overcome these issues, a trained ANN controller is initially used here to enhance motor drive performance. Subsequently, the performance is further improved by modifying the activation function in the ANN controller. Here, motor parameters at rated and variable speed with various loading conditions have been analyzed and compared for the DTC with a conventional PI controller with ANN, and a proposed ANN controller. Simulation of the complete model with the conventional and proposed controllers is done using MATLAB/Simulink platform to observe the various speed responses for different conditions, and the experimental setup is used to demonstrate the effectiveness and performance of the proposed system.
Volume: 16
Issue: 4
Page: 2197-2211
Publish at: 2025-12-01

Integration and optimization of grid through ANN-based solar MPPT and battery

10.11591/ijape.v14.i4.pp988-998
Kolli Sujran , Ankala Sirisha , Ganapaneni Swapna , Malligunta Kiran Kumar , Kambhampati Venkata Govardhan Rao
Integration of solar energy into the grid is the most important aspect for achieving sustainable energy systems. This paper presents an artificial neural network-based maximum power point tracking (ANN-MPPT) system with battery storage to enhance grid efficiency. The proposed ANN-MPPT is dynamically adapted to the varying irradiance and temperature, hence ensuring optimal power extraction from the photovoltaic system. Excess energy is stored in batteries during high solar radiation and discharged when solar generation is low or grid demand is high, maintaining a stable power supply. This system enhances the grid performance in terms of supporting real-time energy exchange, load balancing, and grid stability. Efficient management of the energy fluctuations ensures reliability even at times of grid failures. Further, integration of ANN-based MPPT with battery storage reduces dependence on non-renewable sources and harmonizes solar energy utilization. It can be achieved through enabling smarter energy management and thus contributing to the resilience and efficiency of a grid for better integration of renewable energies. The proposed system can tolerate fluctuating grid demands apart from supporting the features of smart grid, hence viable for increasing stability and sustainability in the grid.
Volume: 14
Issue: 4
Page: 988-998
Publish at: 2025-12-01

Eco-friendly innovation: green energy empowered by IoT

10.11591/ijape.v14.i4.pp903-911
Nikita Amoli , Jitendra Singh , Rahul Mahala , Rajesh Singh , Anita Gehlot , Mahim Raj Gupta
Energy demand is high globally, impacting daily life and promoting sustainable modernization. Goal 9 aims to build an elastic framework for economies, while Goal 15 of the Sustainable Development Goals (SDGs) emphasizes the preservation of terrestrial environment, sustainable woodland management, and biodiversity conservation. The International Energy Agency predicts a significant increase in global renewable capacity, with solar PV being two-third of this growth. Green technology is crucial to combat global warming and Industry 4.0, a digital transformation that aims to create a strong framework for sustainable modernization. The growth of the smart grid is vital, involving energy sources, control techniques, computation, generation, transmission, distribution, and more. Supercapacitors store and deliver energy at high capacity, while green energy transforms fossil fuels into eco-friendly sources using natural resources like hydro, solar, wind, thermal, and biomass. This study explores the efficient use of microprocessors in solar and wind energy, as well as the application of actuators in the green energy sector. Green energy is a sustainable solution to increasing energy needs, reducing dependence on fossil fuels. IoT technologies, including sensors, actuators, microprocessors, and microcontrollers, are used in energy generation, transmission, distribution, and composition.
Volume: 14
Issue: 4
Page: 903-911
Publish at: 2025-12-01

Asymmetrical nine-level hybrid multilevel inverter design and analysis for electric vehicle applications

10.11591/ijape.v14.i4.pp1023-1034
Gerri Ratnaiah , Ramya Ganesan
A novel type of single-phase hybrid multilevel inverter (HMLI) is proposed in this paper. A hybrid system is made up of a multilevel inverter coupled to an H-bridge unit and which can generate nine-level output. To synthesize an output voltage waveform with nine steps, this setup uses merely seven power switches, two diodes, and two DC supplies. A greater number of steps were achieved in output voltage through suggested circuit with a smaller number of components than other existing multilevel inverter (MLI) topologies. A finer output waveform that is closer to a sinusoidal shape is produced with less total harmonic distortion (THD) because of the greater number of steps in the output voltage. Furthermore, it prolongs the switches' lifetime and lowers the voltage stress across them, increasing reliability. In addition, the system produces fewer switches than necessary, resulting in lower power losses and increased efficiency. This guarantees the suggested system's small size and inexpensive cost. A comparison between the suggested topology and the most current MLI topologies has been conducted to highlight the key components of the proposed topology. The suggested topology has been controlled using three distinct controlling schemes are phase disposition-pulse width modulation (PD-PWM), phase opposition disposition-PWM (POD-PWM), and alternative phase opposition disposition-PWM (APOD-PWM).
Volume: 14
Issue: 4
Page: 1023-1034
Publish at: 2025-12-01

Performance enhancement using sensor and sensorless control techniques for a modified bridgeless Ćuk converter-based BLDC motor in EV applications

10.11591/ijape.v14.i4.pp769-782
W. Margaret Amutha , S. Premalatha , M. Karthikeyan
This work proposes a solar photovoltaic (PV)-powered, modified bridgeless Ćuk converter tailored for electric vehicle applications. It overcomes limitations such as high ripple, reduced power density, significant switching losses, and complex circuit structures in traditional designs. The system integrates a boost converter with a bridgeless Ćuk topology to ensure a reliable and efficient direct current (DC) power output. Performance evaluation includes sensor-based and sensorless speed control techniques-pulse width modulation (PWM), proportional integral derivative (PID), back electromotive force (EMF), and spider controllers-under both no-load and full-load scenarios. Key parameters such as rise time, overshoot, settling time, and steady-state error are analyzed. MATLAB/Simulink simulations indicate that the spider controller delivers superior dynamic behavior and stability. A 48 W, 1500 rpm hardware prototype confirms the simulation outcomes, demonstrating the practical viability and effectiveness of the proposed converter.
Volume: 14
Issue: 4
Page: 769-782
Publish at: 2025-12-01

Effect on saturated and unsaturated fatty acids on various vegetable oils on droplet combustion characteristic

10.11591/ijape.v14.i4.pp980-987
Dony Perdana , Muhamad Nur Rohman , Mochamad Choifin
Vegetable oils have composed of triglycerides, which one consist of 3 fatty acids combined with glycerol. Each saturated and unsaturated fatty acid has a different effect on burning characteristics. This study aimed to investigated effect of fatty acids at ceiba pentandra and jatropha oils on the flame behavior of the droplet combustion process. The combustion characteristic was observed by an ignited droplet at the junction using a thermocouple and a high-speed camera (120 fps). Results showed that a higher saturated fatty acid content resulted in long-life and steady flames. This is because more oleic and linoleic acid carbon atoms leave the droplet area and react with air. Jatropha oil produces a higher temperature of 780 °C than ceiba pentandra oil. Temperature of a vegetable oils flame is influenced by number of carbon chains, double bond, and heating value. Ceiba pentandra oil has a higher burning rate of 0.185 mm/s than jatropha oil at 0.155 mm/s. The chain content of polyunsaturated fatty acids has significant effect on rate of combustion, which is due to the weak van der Waals dispersion forces, such that heat absorption is more active and energetic. The highest flame height for ceiba pentandra oil is 55.03 mm compared to for jatropha oil it is 46.82 mm. Long-chain unsaturated double bonds and glycerol cause micro-explosions. This micro-explosion caused the shape of the flame to split and expand so that evaporation occurred faster, thus increasing the size of the flame.
Volume: 14
Issue: 4
Page: 980-987
Publish at: 2025-12-01

A hybrid one step voltage-adjustable transformerless inverter for a one-phase grid incorporation of wind and solar power

10.11591/ijape.v14.i4.pp951-959
Bonigala Ramesh , Madhubabu Thiruveedula , Rahul Inumula , C. Poojitha Reddy , Mohammad Abdul Khadar , K. Sri Sai Hareesh
This paper presents a hybrid one-step voltage-adjustable transformerless inverter designed to efficiently integrate both solar photovoltaic (PV) and wind energy sources into a single-phase grid. The primary objective is to enhance power conversion efficiency while minimizing system complexity and cost. The proposed architecture combines a buck-boost DC-DC converter with a full-bridge inverter in a compact and modular design, enabling voltage regulation across a wide input range typical of hybrid renewable systems. By grounding the PV negative terminal, the system effectively eliminates leakage currents and ensures compliance with IEEE harmonic standards. The inverter operates with reduced switching losses and supports multiple operational modes tailored for variable solar and wind conditions. Simulation of a 300 W prototype demonstrates reliable performance, achieving a total harmonic distortion (THD) below 1%, validating its compatibility with grid requirements. Key contributions include the development of a unified topology for hybrid energy sources, in-depth analysis of energy storage components, and implementation of efficient modulation strategies. This work addresses significant challenges in renewable energy integration and provides a scalable solution for next-generation grid-connected hybrid power systems.
Volume: 14
Issue: 4
Page: 951-959
Publish at: 2025-12-01

Optimize the position of the distributed generator and capacitor bank in the distributed grid to minimize the generation cost

10.11591/ijape.v14.i4.pp970-979
Ngoc An Luu , Dinh Chung Phan
In this paper, we focus on determining the optimal position and size of multi-distributed generators and capacitor banks to minimize the generation cost of a distributed grid. The optimal position and size of distributed generators and capacitor banks are determined using a hybrid of conventional loss sensitivity factor and an improved one. The proposed algorithm has two stages. For each distributed generator, we prioritize its position and size. After that, we find the optimal position and size of the capacitor banks corresponding to this distributed generator installation to minimize the power loss. After considering all distributed generators, the optimal number, position, and size of the distributed generators and capacitor banks are determined based on the minimum generation cost value. This idea is developed in MATLAB and verified via sample distributed grids, including the IEEE-69 bus and IEEE-85 bus. The verifying results are evaluated and analyzed. By comparing those results to those of other methods, the performance of the newly introduced method is proven.
Volume: 14
Issue: 4
Page: 970-979
Publish at: 2025-12-01

Power smoothing in electrical distribution system using covariance matrix adaptation evolution strategy of aquila optimization

10.11591/ijape.v14.i4.pp842-858
Smrutirekha Mahanta , Manoj Kumar Maharana
This study introduces a novel hybrid optimization approach covariance matrix adaptation evolution strategy of aquila optimization (CMAESAO) to enhance power smoothing and minimize power losses in electrical distribution systems through the optimal allocation of D-STATCOMs. The method is tested on standard 33-bus and 69-bus systems. The CMAESAO algorithm efficiently identifies optimal locations and sizes of D-STATCOMs to achieve system performance improvements under constant power (CP), constant current (CC), and constant impedance (CI) load models. The results show that, for the 69-bus system, installing two D-STATCOMs yields optimal performance, reducing real power loss from the base value to 149.6368 kW, while three D-STATCOMs yield a slightly better voltage profile and VSI but only marginal additional power loss reduction (147.8951 kW), making two units more cost-effective. For the 33-bus system, three D-STATCOMs provide the best improvement in power quality and loss minimization. Voltage and current profiles confirmed improvement in voltage stability and reduced branch currents with optimized placements. Compared to other optimization techniques, CMAESAO demonstrates faster convergence and superior accuracy in minimizing losses, establishing its effectiveness for such multi-objective optimization problems. The study's novelty lies in integrating CMA-ES with aquila optimization to combine strong global search with adaptive exploration, resulting in robust and efficient power system enhancement. The proposed methodology contributes to smarter, more reliable distribution systems, supporting grid resilience and energy efficiency.
Volume: 14
Issue: 4
Page: 842-858
Publish at: 2025-12-01

Frequency control of hybrid power system with fractional order secondary controller using improved biogeography-based krill herd algorithm

10.11591/ijape.v14.i4.pp816-825
Kukkamalla Kiran Kumar , Gobinathan Balaji , Kanta Rao Pedakota , Majahar Hussain Mahammad , Syed Suraya
To meet the demand of electrical power, structural changes of the power system from the generation side are necessary by integrating the renewable sources into the existing system. In the presence of renewables, the active power imbalances caused by both generation and demand are reduced with the classical units (like thermal) since the wind speed and irradiance (inputs of wind and solar plants) are volatile and nonlinear in nature. The frequency deviations triggered by such active power imbalances of the hybrid power system integrated with both conventional and renewable energy plants are minimized with better secondary control schemes. Therefore, this article suggests fractional order secondary controller (FOSC) for conventional units of the interconnected power system to strengthen the frequency stability of the system during the demand perturbations. The optimal gains of the FOSC are identified with an improved biogeography-based krill herd optimizer with the help of the performance indicator integral square error. To elevate the improvements of FOSC, comparisons are provided with classical controllers during the simple, random load perturbations with and without generation changes. Furthermore, sensitivity analysis on system parameters is performed to show the robustness of the FOSC over classical control strategies.
Volume: 14
Issue: 4
Page: 816-825
Publish at: 2025-12-01
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