Environmental awareness, coupled with tightening regulations regarding greenhouse emissions, is putting pressure on the automotive industry to shift towards sustainable propulsion methods, with vehicle electrification emerging as a feasible solution for effective CO₂ reduction. As Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), and Plug-In Hybrids (PHEVs) find their place in the market, standardization efforts by international organizations such as SAE, CHAdeMO, and IEC have been made in developing charging standards; however, the grid-scale adoption of EVs poses significant challenges in terms of harmonic generation, increased peaks in demand, and voltage fluctuation at the point of supply. High-speed charging beyond 150 kW entails the use of highly efficient step-up AC-DC converters that also adhere to IEC 61000-3-2 harmonic limits. These requirements are not easily met by existing rectification techniques, including the traditional single-stage three-phase rectifier topology, Modular Multilevel Converter, and Vienna Rectifier, all of which face trade-offs between increased circuit complexity, component costs, current imbalance, and power losses. In this paper, a three-phase modular switched-capacitor Power Factor Correction (PFC) converter that generates an output voltage of 1600 V from a 230 V AC per-phase supply is presented. The proposed converter consists of three phases, each containing a full-wave rectifier bridge, two boost inductors, one active switch, and a three-level switched capacitor voltage multiplier. The phase modules operate in quadrature relative to each other to ensure symmetrical input currents. The switched capacitor-based configuration reduces the voltage stress across the devices to a small portion of the output level. This translates to reduced device costs and thermal management issues compared to existing PFC topologies. Dual-loop average current control architecture is adopted to provide regulation of the DC bus voltage and power factor correction through input current shaping. The simulation results obtained in MATLAB/SIMULINK prove that the output current waveform produced by the converter contains very little harmonics with the total harmonic distortion being only 5.38% when considering the fundamental value of 18.63A, which conforms to the limit imposed by IEC 61000-3-2. The output voltage shows good regulation with a constant value of 1600V under steady-state and dynamic load variations. Hardware testing of the designed converter on the Opal-RT system has verified the results from the computer simulations. It can be concluded that the developed converter is applicable for high-power electric vehicle fast charging stations.
This article presents the design and development of an electronic module based on the ESP32 microcontroller for the purpose of unifying and interconnecting various industrial networks. The architecture integrates protocols widely used in today's industry, such as Modbus RTU using the ADM485 transceiver, Modbus TCP/IP through the ENC28J60 Ethernet controller, CAN Bus communication using the MCP2551 transceiver, and DeviceNet networks using a combination of the MCP2515 and MCP2551, using the ESP32 microcontroller. It also incorporates compatibility with Profibus networks using the MAX14770 transceiver, as well as integration of smart instrumentation using the HART protocol employing the AD5700 modem and serial protocols such as RS232 and RS485.The electronic module enables bidirectional communication between industrial equipment using the most commonly used communication protocols in the industry today, facilitating the modernization of industrial equipment such as frequency converters, PLCs, or input and output expansion modules in existing systems without the need to completely replace this equipment. The module contributes to unification by selecting the appropriate network for a device or equipment and to the digitization of processes in Industry 4.0, achieving improvements such as interoperability, robust connectivity, and integration into multiple equipment without the need to make changes to their hardware. The electronic module adapts to an industrial environment and integrates 75% compatibility between devices such as PLCs, frequency converters, and I/O expansion modules.
This paper the nonlinear response of a 65 nm MOSFET in moderate inversion and saturation regions is analyzed through a mathematical formulation. Harmonic components are derived from the drain current expression by employing modified Bessel functions. These results are compared with numerical simulations performed in the NGSpice environment using the BSIM4 library. The simulations allow us to verify the appearance and magnitude of the harmonics found analytically. In addition, the analysis is replicated using MATLAB, facilitating parametric evaluation and detailed visualization of the current harmonic. The bias conditions are also analyzed to minimize unwanted harmonics, for which criteria are evaluated that allow the design of analog circuits with low distortion. Finally, all associated codes and findings have been made available to the public in an online repository.
Locating Underwater Locator Beacons (ULBs) in deep-sea conditions is still a significant challenge due to the severe Attenuation (acoustic) and complex multipath transmission, and the presence of high ambient ocean noise. Current localization methods tend to be based on simplified acoustic propagation models or computationally intensive physics-based solvers, thus limiting the accuracy of localization and the ability to deploy it in real-time in practical Search and Rescue (SAR) missions. In a bid to overcome these shortcomings, this paper suggests a physics-informed hybrid optimization framework of localizing black boxes in the deep sea through a combination of passive sonar modeling and a hybrid Marine Predator Algorithm-Harris Hawks Optimization (MPA-HHO) strategy. The proposed structure uses the standard passive sonar equation and realistic transmission loss models that consider the effects of spherical spreading, frequency-dependent absorption (approximately 6.57 dB/km at 37.5 kHz), and major deep-water propagation processes such as surface reflection, surface ducting, bottom bouncing, convergence zone propagation, deep sound channel effects, and dependable acoustic paths. The proposed hybrid model is in contrast to traditional standalone optimization models that typically do not incorporate the global exploration properties of MPA or the effective local exploitation properties of HHO to enhance localization accuracy and convergence behavior. The results of the simulation under the realistic conditions of a deep-ocean environment show that the proposed structure reaches a high level of fitness of 10.1 dB and that it converges in almost 45 iterations. The distance error is minimized to 0.03 km with a probability of detection of 0.98 in a 3 km range. Moreover, the framework reduces the search area of 10,000 km² to about 3.14 km2, which corresponds to over 99.9% search-space reduction, and only requires 11.8 seconds of execution time, a nearly 87% reduction in computational complexity compared to traditional ray-tracing techniques. The findings prove that the incorporation of realistic ocean acoustic physics with adaptable hybrid bio-inspired optimization has offered a robust, computationally efficient and reliable solution to deep-sea underwater locator beacon detection and near real-time SAR operations.
Accurately forecasting solar power is essential for secure and efficient operations of contemporary power systems, particularly in the smart grid and renewable energy integration context. In this paper, we present a new hybrid deep learning model, the BiLSTM Model using Improved PSO (BiLSTM-IPSO), for improved short-term prediction of solar power, which incorporates the Bidirectional Long Short-Term Memory (BiLSTM) model with an Improved Particle Swarm Optimization (IPSO) algorithm. The IPSO algorithm successively arranges the BiLSTM hyperparameters with chaotic initialization, adaptive inertia weights, and velocity clamping, to converge globally and avoid stagnating locally. The proposed BiLSTM-IPSO model performance is compared with various state-of-the-art techniques, including TLBO-DL, CNN-LBO, BiLSTM-AADC, and HCLN, using standard indicators, such as MSE, RMSE, MAE, MBE, and R². Results show that our model consistently performs better than all the baselines, and the lowest RMSE and the highest R² are 0.0565 and 0.955, respectively, on the 0.5-hour horizon. The excellent generalization performance of the framework in all of its forecast horizons indicates its feasibility for applications in real-time deployment in solar energy management systems and smart-grid operations.
Transmission congestion in present-day heavily-loaded and deregulated power systems acts as a major bottleneck to economic power exchange and severely jeopardizes system reliability. Congestion management is part of maintaining market efficiency and system security. This paper describes an optimization technique for relieving transmission congestion through the optimal rescheduling or redispatch of active power generation. The problem is a nonlinear, constrained optimization aimed at minimizing the total cost of generation redispatch with strict adherence to the constraints of the system. Given the inherent high-dimensionality and complex constraints in power system optimization problems, a newly introduced efficient metaheuristic algorithm known as the Spider-Tailed Horned Viper Algorithm (STHVA) is used to discover the solution space for the global or near-global optimum. The basic version of the algorithm is enhanced as Levy Flight-based Spider-Tailed Horned Viper Algorithm (LESTHVA). The robust concert of the proposed method is tested on the IEEE 30-Bus test system and demonstrates its ability to quickly and efficiently relieve any line overload with minimal penalties in operational costs, thus setting up a practical framework for real-time system operation and resource utilization.
The quality upsets of power quality are major operation concerns of Micro-Grid systems due to the increasing use of renewable sources of power, power electronic converters, and dynamic loads. despite the variety of signal-processing, machine-learning, and deep-learning methods that have been suggested regarding disturbance detection and classification, the literature is still disjointed in terms of datasets, evaluation criteria, and real-time implementation. The review is a scientific study of the literature on the subject of power quality disturbance monitoring in micro-grids through a prisma-guided methodology, and the reviews were refined according to the established inclusion and exclusion criteria. The selected works are compared in terms of the nature of disturbance, the tool of extracting the features, the classifier, the dataset, the performance evaluation, the computer complexities, and the capability to implement it. The review shows that hybrid and deep-learning-based models are highly classified, yet limited by the problem of data imbalance, unavailability of real-life three-phase datasets, low noise tolerance, and low interpretability. It is on this synthesis that the paper reflects the key gaps in the research and recommends the roadmap of the future of robust, real-time, and scalable micro-grid power quality monitoring.
Variable-speed operation and partial-scale power converter capabilities are the primary reasons for the growing popularity of Wind Energy Conversion Systems (WECS) that are based on Doubly-Fed Induction Generators (DFIG). These systems are both cost-effective and efficient. However, the rotor windings are subject to substantial Electromotive Forces (EMF) when the grid voltage decreases, which can lead to excessive rotor currents, converter saturation, and the potential for grid disconnection. The conventional mitigation solutions, such as crowbar circuits and PI-based controllers, either limit the controllability of the system or lack the resilience necessary to withstand large shocks. The Sliding Mode Control (SMC) technique is proposed in this study for the rotor-side converter to modulate rotor currents and reduce induced electromagnetic fields during symmetrical and asymmetrical voltage fluctuations. Compliance with the control law is verified by testing the Low-Voltage Ride-Through (LVRT) grid code requirements, which incorporates a boundary layer to reduce chattering and features a boundary layer. This ensures robust monitoring of current references. In comparison to conventional controllers, the suggested method is shown to reduce rotor current overshoot dramatically, preserve DC-link stability, and offer fast reactive power assistance. These findings were derived from simulations carried out in MATLAB/Simulink. The results indicate that the proposed SMC is both practical and effective in enhancing the fault ride-through capability of DFIG-based WECS.
Agriculture is a key source of income for a large section of the world's population. It has become a necessity to make prompt and accurate pest detection for overall crop protection along with sustainable farming. Several current research projects rely on CNN architectures or cloud-based systems, which may not be suitable for agricultural fields with limited resources. To address this gap, the paper proposes a novel hybrid ViT-SVM model for real-time pest identification. This combines Vision Transformer, having global feature extraction capabilities, with Support Vector Machine, which has robust decision limits. Extensive tests were undertaken to evaluate several feature extractor-classifier mixtures. These combinations include ResNet, DenseNet, and ViT, combined using both SVM and Random Forest classifiers. To guarantee statistical reliability, F1-score, recall, accuracy, and precision were the metrics used to assess performance. Macro, weighted averages, class-wise ROC-AUC, PR-AUC, confusion matrices, McNemar tests, as well as bootstrap confidence intervals were calculated to test the performance of the models. The ViT-SVM hybrid system achieved superior performance to all other systems because it reached 96% accuracy and achieved an F1-score while maintaining equal success rates in detecting visually similar yet less common pest species. The system was developed as a model that ran on a Jetson Nano human-operated robotic system known as AgroPestBot, which used an Android interface for real-time pest identification at field sites. The method delivers an effective edge-AI solution that combines advanced machine learning capabilities with agricultural technology that can be used in the field to deliver farmers instant, precise pest detection results. The study demonstrates how lightweight hybrid models can be applied to resource-limited environments while establishing base technologies for upcoming automated systems and improved, precise agricultural methods.
Usually, when describing the impact of signals, the informational approach and the thermal analysis approach are applied separately. This is due to the fact that the tasks of information suppression and thermal damage caused by high-power signals have traditionally been considered independently. However, in the case of using complex fractalized destructive signals, it is justified to apply an integrated approach. In particular, it is reasonable to employ not only a model describing the process of response formation of a radio-engineering system to low-amplitude electromagnetic information signals with a chaotic temporal structure, but also to assess, from the standpoint of the thermodynamic formalism, the possibility of the emergence of destructive chaotic regimes in the electrical circuits of radio-engineering systems. The object of the study is the process of propagation of fractalized destructive signals through the electrical circuits of radio-engineering systems. The article presents the results of the study on the processes leading to the emergence of chaotic regimes in the receiving path of radio-electronic devices under the influence of an electromagnetic pulse penetrating through the antenna-feeder system. The application of corresponding mathematical models and equations describing the dynamics of transition to chaotic states is considered. The analysis of fractal binary sequences revealed statistical patterns that correlate with the characteristics of complex natural and artificial systems. This opens up prospects for applying methods of generalized statistical thermodynamics in the study of complex information processes, which may be useful for a comprehensive understanding of their structure and dynamics. The obtained results support the hypothesis of a hierarchical organization of such sequences and their invariant nature.
Intelligent sensing and communication systems are increasingly used in autonomous and connected vehicles, which present new challenges in data privacy and cyber-threat detection in smart city environments. This work proposes an AI-driven multimodal security framework that integrates sensor fusion, edge-cloud coordination, federated learning, and adversarial robustness for reliable and privacy-preserving threat detection. The proposed methodology integrates multimodal feature embedding, secure aggregation of parameters, hybrid anomaly scoring, and adaptive decision flow across the edge-cloud layers. Experimental results demonstrate promising performance with 97.8% detection accuracy, 96.9% F1-score, 93.6% robustness, and 98.3% privacy preservation, showing very stable results in all scenarios, such as urban, highway, nighttime, and noisy weather. Performance comparison does present palpable gains over state-of-the-art IDS and the existing federated model. This work is thus concluded with the belief that distributed intelligence integrated with resilient multi-model analytics significantly strengthens vehicular security with a scalable and future-ready solution for smart city ecosystems.
Blur detection is an essential yet challenging task in digital image processing, particularly in medical imaging applications where image quality directly affects analysis reliability. In Digital Breast Tomosynthesis (DBT), motion artifacts and limited-angle acquisition frequently cause image blurring, degrading fine structural details, and complicating automated analysis. Blur detection in DBT is further challenged by the lack of prior blur knowledge and the visual similarity between blurred and sharp regions. This paper proposes a hybrid feature-based CNNSVM approach for detecting blur in DBT images by jointly utilizing automated image features extracted from a CNN with a BF (Laplacian-based Blur Factor). The BF, based on the variance of the Laplacian response, is applied to measure edge degradation and is used in conjunction with CNN-based features to enhance classification performance. The integrated feature set is characterized by a Support Vector Machine (SVM) model to identify blurred or non-blurred DBT images. Experimental evaluation using a large publicly available DBT dataset indicates that the hybrid scheme achieves an accuracy of 99.21% in blur detection. The model performed much better than CNN and CNNSVM models that rely only on image features. Furthermore, the hybrid design reduces complexity and maintains good performance at reduced training times. These findings indicate that the proposed framework provides a practical way to automatically blur detection in medical image processing tasks.
This study examines the need for efficient Radio Frequency (RF) energy harvesting as a renewable alternative energy source. This study's main objectives are to design, produce, and test a rectenna system that combines a seven-stage voltage multiplier rectifier with a microstrip Yagi-Uda three-element antenna. The purpose of the rectenna is to transform received radio frequency energy into DC power that may be used by low-power electronic equipment. Experiments in the form of circuit design using NI Multisim, antenna design using CST Studio Suite, fabrication, and testing are all part of the study technique. With particular goals for strength (≥ 5 dBi), VSWR (1 < VSWR ≤ 2), and return loss (<- -10 dB), the antenna is made to receive signals in the Ultra-High Frequency (UHF) band. Schottky diodes, which are perfect for converting RF frequencies to DC because of their low forward voltage and quick switching speed, are used in the rectifier. The antenna's efficacy has been validated through both simulation and experimental outcomes. These results indicate a return loss less than -10 dB, a Voltage Standing Wave Ratio (VSWR) ranging from 1.1 to 1.8, and a gain exceeding 5 dBi, all of which signify acceptable signal integrity and impedance alignment. Investigations into the reception of digital television broadcasts, coupled with laboratory assessments, revealed that the rectenna functions effectively at varying proximities to the radio frequency emitter, generating a direct current output whose voltage escalates with increased input power. The system achieves its peak output of 1.696 V at a distance of 1 kilometer from the Digital Terrestrial Television (DTV) transmitter and 1.80 V when situated 25 centimeters away with an input power of 20 dBm. The system's capacity to energize Light-Emitting Diodes (LEDs) as a load underscores its potential for powering low-consumption apparatus.
The aim of this research is to enhance the amount of power captured from Photovoltaic (PV) systems by tracking the utmost power in partial shading conditions and uninterruptedly regulating the PV systems to function at the Maximum Power Point (MPP) in climate variations like temperature and irradiance. This study introduces an intelligent MPPT system focused on Constant Power Generation (CPG) that combines an Active Clamp Triple-Stage Cascade (ACTSC) Boost converter with a Radial Basis Function Neural Network (RBFNN) controller with the Pied Kingfisher Optimization Algorithm (PKOA). The ACTSC Boost converter increases voltage gain and reduces losses on the switch, resulting in excellent efficiency. In order to quickly monitor the MPP with fewer steady-state oscillations, the PKOA-optimized RBFNN-MPPT controller constantly modifies its weights and spread parameters. PKOA improves MPPT accuracy under varying irradiance and temperature conditions by ensuring faster convergence, improved flexibility, and global search capabilities. The higher performance of the proposed approach over traditional MPPT algorithms is confirmed by an extensive MATLAB tool, highlighting that the proposed approach has a better efficacy of 94.33%and superior tracking efficiency, thereby offering a reliable and effective solution for contemporary PV applications in grid-connected systems.
In renewable energy systems, including wind, solar, Bio-Electrochemical, and Artificial Intelligence (AI), are increasingly being applied. In this paper, the most evident issues of producing, regulating, and manufacturing renewable energy by AI are explored. We examine the different AI techniques, including machine learning, deep learning, reinforcement learning, and hybrid techniques, and their applications to forecasting, optimization, predictive maintenance, and grid management. The review paper discusses the applications of solar, wind, and hydropower in different areas and identifies major concerns of data quality, cybersecurity, and scalability. AI not only makes things work better technologically but also promotes green innovation and assists us in achieving the Sustainable Development Goals. We summarize strategic suggestions and possible future research directions in this rapidly developing field.
This paper introduces a Reduced-Order Observer–Based Optimal Controller (ROOBC) strategy integrated for a Thyristor-Controlled Series Compensator (TCSC) to effectively enhance the dynamic stability of a unique system. The proposed control scheme employs an ROOBC to estimate the unmeasured system states, like rotor angle, thereby lowering computational complexity while maintaining effective control performance. An Optimal State-Feedback Control (OSFC) law is designed based on the estimated states to improve the system dynamic response under disturbance. The effectiveness of the proposed ROOBC-based TCSC controller is checked on a unique system and compared with conventional TCSC control, Power System Stabilizer (PSS), and the system without control. Stability assessment is carried out through eigenvalue analysis and time-domain simulations at the nominal operating condition. The results demonstrate that the proposed ROOBC-based TCSC significantly enhances dynamic stability and provides superior dynamic performance compared to the other control strategies considered.
In Wireless Sensor Networks (WSNs), Energy optimization focuses on lessening energy consumption to lengthen the network’s lifetime. Nevertheless, the existing studies did not perform a Vice Super Cluster Head (VSCH) with secure handover when the Super Cluster Head (SCH) energy is dropped. Thus, this paper proposes Super Cluster Head Vice Super Cluster Head (SCH-VSCH) selection-enabled Energy-Efficient (EE) optimal path selection in WSN using Log Adjustable Fuzzy Logic System (LA-FLS) and Baker Map Sand Cat Swarm Optimization (BM-SCSO). Initially, the WSN nodes are initialized randomly. Then, by using Clipped Voronoi Diagram-Sinusoidal Sigma Representation Yule’s K-Means clustering (CVD-2SRY-KMeans), the redundant Sensor Nodes (SNs) are reduced. Next, based on LS-FLS, the SCH-VSCH is selected. Afterward, between the source and destination, the hop count is estimated. Then, by using the Log Hop First Scheduling Algorithm (LHFSA), the nodes are scheduled according to the hop count. Later, by using the Ad hoc On-demand Distance Vector (AODV) protocol, the possible routes for the packet transmission are generated. Currently, from the SNs, the data is sensed. The congestion of the node is estimated by using LA-FLS according to the packet arrival rate. If congestion is present, then it is minimized by the backpressure method. Lastly, by employing BM-SCSO, the optimal path is selected. SCH’s position is securely handed over to VSCH by the Doubling-Doche-Digital-Oriented-Icart-Kohel Signature Algorithm (D3-OIKSA) if the SCH energy drops. Next, for reliable packet transmission, the remaining steps are processed. As per the results, the proposed framework achieved a high throughput of 56825.74916 bytes, thus outperforming prevailing techniques.
This study emphasizes an improvement method for Simplified Routh Approximation with discrete-time SISO interval systems. The diminished order model's numerator and denominator are estimated using the θ interval table. The Kharitonov polynomial is also used to verify the proposed inferred interval model's stability. The enhanced approach sustains the reduced system's stability feature if the system of high-rise order intervals is sturdy. The limitation of calculating the reciprocal transformation and inverse reciprocal transformation, observed in the very recent Advanced Routh Approximation Method, has also been avoided in the suggested procedure. To substantiate the relevance of the initiated approach, plotting of impulse and step responses for the reduced models as well as the system. A mathematical example is comprehended to elucidate the proposed method and simplified to the Simplified Routh Method, Model order in discrete time order, uncertain method, and mixed method (α and β method) of integral square error values. To obtain the results, interpret the efficacy and accuracy of this intended approach.
Nowadays, power converters are becoming crucial in the usage of renewable energy sources. In this paper, three diverse DC-DC converters have been discussed, which provide high gain with fewer components and are economical. Using MATLAB/Simulink, these three different topologies have been compared in terms of gain and Total Harmonic Distortion (THD) using various sources of energy. The topologies are incorporated with only one switch the losses can be reduced. The findings indicate that DC-DC Converters with an input voltage of 48V are thought to attain up to a gain of about 10 when compared with other topologies. With this, numerous high-gain DC-DC converters are being extended to induction heating applications. Furthermore, THD of 0.5% or less is guaranteed by the recommended topology. And also, these DC-DC Converters are mostly utilized in Electric Vehicles (EVs) and Uninterruptible Power Supply (UPS). Additionally, their small size, controllability, and increased efficiency make them appropriate for integration with high-frequency inverters, smart home energy interfaces, and microgrid systems that rely on renewable energy sources. According to simulation results, the converter's dynamic performance and stability make it dependable for use in both residential and commercial energy applications. They are appropriate for grid-connected applications and next-generation smart energy systems due to their straightforward control mechanism, scalability, and excellent performance under dynamic load conditions.
Helical antenna is prominently used in wireless communication due to the prominent properties of helical antenna with reference to the gain, polarization, and simpler geometry. In this paper, the work is focused on the frequency reconfiguration capability of the helically loaded hexagonal cavity-backed antenna. The optimization of helical antenna physical parameters is carried out in this research. The helical antenna parameters taken into consideration are bandwidth, gain, and radiation pattern in the 3–10 GHz frequency range. Frequency reconfiguration is achieved by using a Varactor diode, which gives the dynamic frequency variation of the resonance frequency of the helical-loaded hexagonal cavity-backed antenna. The proposed antenna provides multiband frequency operation in dynamic mode for wireless applications. The varactor diode demonstrates the frequency-shifting capabilities by increasing the electrical length of the antenna. By integrating the diode between two turns of the helical antenna, the antenna resonating frequencies are shifted from higher frequency operation bands toward lower frequency bands. The four resonance frequencies, 5.68 GHz, 6.99 GHz, 8.85 GHz, and 9.49 GHz, are shifted to lower frequencies of 3.90 GHz, 5.75 GHz, 6.85 GHz, and 9.63 GHz by integrating a varactor diode. The additional four resonance frequency bands are achieved, and that is on the lower frequency side.
