International Journal of Power Electronics and Drive Systems (IJPEDS)
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Adaptive intelligent PSO-Based MPPT technique for PV systems under dynamic irradiance and partial shading conditions
Get PDFThis research introduces an adaptive improved particle swarm optimization (AIPSO) approach for maximum power point tracking (MPPT) approach designed to enhance energy harvesting from photovoltaic (PV) systems under dynamic irradiance conditions. The proposed AIPSO algorithm addresses the challenges associated with traditional MPPT methods, particularly in scenarios characterized by fluctuating solar irradiance, such as step changes and partial shading. By incorporating a robust reinitialization strategy along with updated velocity and position equations, the algorithm demonstrates superior performance in terms of convergence accuracy, tracking speed, and tracking efficiency. This modification enables the algorithm to effectively escape local maxima and explore a wider search space, leading to improved convergence and optimal power point tracking. Furthermore, the adaptive nature of the PSO enhances the algorithm’s ability to respond to real-time changes in environmental conditions, making it particularly suitable for large- scale PV systems subjected to varying atmospheric factors. Here, “adaptive” denotes coefficient scheduling (C3) and a re-initialization trigger that responds to irradiance regime changes; “intelligent” denotes robust regime shift detection and safe duty ratio clamping. Across uniform, step change, and partial shading conditions, the proposed AIPSO achieves fast reconvergence and high tracking efficiency with negligible steady state oscillations, as summarized in the results. Building on this contribution, future research will focus on evaluating its scalability across different PV architectures and large-scale grid integration with real hardware setup
A review of modeling techniques and structural topologies for double stator permanent magnet machines
Get PDFThis study reviews the advancements in double-stator permanent magnet machines (DSPMM) with a focus on modeling techniques, design variations, and performance optimization. The research categorizes existing DSPMM modeling methods, including numerical approaches like finite element method (FEM) and boundary element method (BEM), as well as analytical approaches such as analytical subdomain method (ASM), magnetic equivalent circuit (MEC), and Maxwell's equation approach (MEA). These methods improve analytical accuracy, computational efficiency, and address challenges like magnetic saturation and electromagnetic interactions. Structural innovations, including segmented rotor-stator techniques, Halbach arrangements, and soft composite materials, enhance torque density, reduce cogging torque, and optimize magnetic flux distribution, contributing to higher energy efficiency and reduced noise. Supported by software tools like Ansys Maxwell and JMAG-designer, this study identifies optimal DSPMM configurations for various applications, including electric vehicles and renewable energy systems. The findings emphasize the potential of DSPMM for efficient, high-performance electric machines while highlighting the need for further research on transient effects and advanced cooling systems to improve thermal stability
An approach of battery adaptation in wireless sensor network with resource aware in extreme environmental area
Get PDFA wireless sensor network (WSN) is a distributed wireless system that employs sensor nodes to perform various tasks, including sensing, monitoring, data transmission, and delivering information to users via internet communication. Resource availability in WSNs is a critical factor influencing data delivery performance. One of the main challenges is the rapid depletion of resources, particularly batteries, which play a pivotal role in the system’s operational sustainability. This study evaluates the impact of battery adaptation through four testing scenarios. The results show that implementing battery adaptation significantly extends system lifespan compared to scenarios without adaptation. In the scenario without both a classification algorithm and adaptation, the system lasts approximately 270 minutes. When battery adaptation is applied without a classification algorithm, the lifespan increases to 330 minutes and 30 seconds. In contrast, the scenario using a classification algorithm without adaptation yields a lifespan of about 185 minutes, while combining the classification algorithm with adaptation extends it to approximately 252 minutes. The findings demonstrate that battery adaptation enhances the longevity and resource efficiency of WSN systems. However, the use of a classification algorithm tends to reduce operational time compared to scenarios that do not employ such algorithms
Optimal annual solar PV penetration for improved voltage regulation and power loss reduction under uncertainty conditions
Get PDFGiven their technological, economic, and environmental advantages, the widespread adoption of renewable distributed generators (RDGs) in distribution systems (DSs) is becoming more prevalent. However, Solar photovoltaic distributed generators (PV-DGs) face the challenge of intermittent behavior, which results in power output fluctuations and increased grid uncertainty. Therefore, addressing these uncertainties is crucial when determining their optimal allocation. The proposed method considers uncertainties related to both load demand and solar irradiation. The model is formulated as a stochastic mixed-integer nonlinear optimization problem, which is solved using the whale optimization algorithm (WOA). The standard IEEE 33-bus system is used to validate the proposed approach, and demand variations are modeled based on the IEEE reliability test system (IEEE-RTS). The objective is to simultaneously minimize total expected voltage deviation, real power loss, and reactive power loss while increasing solar PV penetration. The technique for order of preference by similarity to the ideal solution (TOPSIS) is applied to select the best solution. Simulated results indicate significant improvements: a 19.39% reduction in voltage deviation, an 18.42% decrease in total real power loss, and an 18.53% reduction in reactive power loss compared to the base case. Additionally, the model accommodates a total of 3.206625 MW of solar PV power in the DS
Buck-boost converter Fed nine level cascaded H-bridge inverter
Get PDFThis research investigates on simulation of a traditional cascaded H-bridge (CHB) five-level inverter and proposes a nine-level cascaded H-bridge inverter system. The performance of both five-level and nine-level inverter systems is evaluated by modeling and simulating the open-loop system. According to the simulation results, the nine-level multilevel inverter (MLI) has a lower total harmonic distortion (THD) than the five-level MLI. The work also introduces a boost converter positioned between a photovoltaic power source and the inverter. A nine-level inverter system is utilized to simulate the proposed photovoltaic and battery-based buck-boost converter (BBC). The effectiveness of the proposed inverter is verified through simulation studies under various scenarios. In terms of THD, the comparison of the open-loop systems indicates that the nine-level inverter performs better than the five-level inverter. Additionally, simulations for a battery-based buck-boost converter and photovoltaic system used to verify the effectiveness of the proposed inverter
A versatile three-level CLLC resonant converter for off-board EV chargers with wide voltage adaptability contribution
Get PDFThe vehicle-to-grid (V2G) concept has gained significant attention in the last decade due to its potential to enhance direct current (DC) microgrid stability and reliability. Electric vehicles (EVs) play a central role in distributed energy storage systems, optimizing efficiency and enabling the integration of renewable energy sources. This study offers a unique three level CLLC resonant converter developed for off-board EV chargers to promote bidirectional power transfer between DC microgrids and EVs. The suggested converter uses resonant CLLC components and two three-level full bridges to effectively handle a broad range of EV battery voltages (200 V–700 V). To ensure effective power conversion, the first harmonic approximation (FHA) model is used to analyse the converter's resonant frequency characteristics. The proposed system achieves high efficiency (>95%), with voltage stability maintained at 750 V under various load conditions. The converter's performance was validated through MATLAB based simulations, comparing proportional integral (PI) and proportional integral derivative (PID) control strategies. The PID-controlled system demonstrated superior dynamic response, reduced current ripples, and enhanced voltage regulation compared to the PI-controlled system. This study demonstrates the viability of implementing a three-level CLLC resonant converter for efficient, bidirectional, and wide-voltage adaptation in EV charging infrastructure, thereby contributing to grid stability and renewable energy integration
Design and analysis of seven-level hybrid modified H-bridge multilevel inverter
Get PDFThis paper introduces a novel boosting multilevel inverter that utilizes switched capacitors. Current multilevel inverters (MLIs) face several issues, such as complex structures, intricate switching controls, and challenges in generating gate pulses, numerous components, and high voltage stress on semiconductors. The increase in the number of levels adds to the complexity and cost of the circuit and can reduce reliability in some cases. The proposed topology creates a 7-level voltage waveform using 9 switches, 1 diode, and 2 capacitors, and it triples the voltage gain. The capacitors maintain self balanced operation without the need for additional circuits. A simple logic gate-based pulse-width modulation (PWM) technique is presented to ensure power balancing of the capacitors. The proposed 7-level switched capacitor boosting multilevel inverter features a reduced switch count, lower voltage stress, and built-in fault tolerance. The paper includes a comprehensive comparison of various related topologies. The proposed topology is simulated in PSIM, with simulation results presented for different parameters
A novel technique for optimization of BLDC-based dual-motor electric vehicles using adaptive BFO-based PID controller
Get PDFThis study addresses the imperative for electric vehicle (EV) propulsion systems to operate at higher speeds with effective motor control, given the rapid advancement of EV technology. Specifically focusing on electric 2-wheelers, we aim to enhance their maximum speed range from 45 km/hr to 110 km/hr by optimizing the control strategy of a widely used commercial e-bike from Vespa. Our approach explores the feasibility of employing a dual motor system instead of a single motor, coupled with optimization techniques for a proportional-integral-derivative (PID) controller governing a linear brushless DC (BLDC) motor. Implemented in MATLAB/Simulink, our method offers advantages such as consistent convergence, ease of implementation, and high computational efficiency. By employing bacterial foraging optimization (BFO) along with an adaptive BFO (ABFO) technique to optimize the PID controller, we achieve significantly faster response times compared to conventional BFO methods. These findings underscore the efficacy of our approach in enhancing the speed control and acceleration characteristics of EV propulsion systems, contributing to the ongoing evolution of electric mobility solutions
Experimental study on the use of Savonius combined blade rotors as wind turbines and hydrokinetic turbines
Get PDFRenewable energy development is increasingly important to anticipate the limited use of fossil energy and its impact on the environment. The Savonius turbine is a vertical axis turbine that can utilize flow from all directions with simple construction, so it has the potential to be developed as a wind turbine and hydrokinetic to generate electricity. This paper aims to conduct an experimental studied the same Savonius combined blade rotor as a wind turbine used in a wind tunnel and a hydrokinetic turbine in an irrigation channel. The experimental results show that the Savonius turbine can function well as a wind and hydrokinetic turbine. The Savonius combined blade turbine improves the performance of conventional Savonius blade turbines, including its use as a hydrokinetic turbine, which is affected by flow velocity. The performance of the Savonius turbine is indicated by the power coefficient Cp and torque coefficient (Ct) values based on the fluid flow velocity. At the same wind speed (4 m/s), the combined blades can increase the performance Cp by up to 11% compared to conventional blades. The use of the same combined blades tested as a hydrokinetic turbine resulted in an increase in Cp and a decrease in Ct with an increase in tip speed ratio (TSR)
Electronic properties of amorphous silicon carbon are correlated with the methane flow rate
Get PDFThis study examines how methane flow rate during the plasma-enhanced chemical vapor deposition (PECVD) process affects the electronic properties of amorphous silicon-carbon (a-SiC) thin films. The films were deposited with varying methane flow rates, and their structural and electronic properties were analyzed using spectroscopic ellipsometry and atomic force microscopy (AFM). Results show that the methane flow rate influences the ratio of sp2 to sp3 carbon bonding, which impacts the material's electronic band structure. Higher methane flow rates increase sp2 carbon content, reducing the bandgap energy and enhancing electrical conductivity. In contrast, lower flow rates lead to higher sp3 bonding, wider band gaps, and decreased conductivity. This study highlights the potential for optimizing methane flow rates in PECVD to tailor the electronic properties of a-SiC films for specific applications. The findings offer valuable insights for designing and optimizing a-SiC materials for electronic devices. Future research will investigate how other deposition parameters and post-deposition treatments affect a-SiC's electronic properties, aiming to further improve material performance for advanced technological applications