In order to solve the problems of slow dynamic response and difficult multi-source coordination of solar electric vehicle charging stations under intermittent renewable energy,this paper proposes a hardware-algorithm ...In order to solve the problems of slow dynamic response and difficult multi-source coordination of solar electric vehicle charging stations under intermittent renewable energy,this paper proposes a hardware-algorithm co-design framework:the T-type three-level bidirectional converter(100 kHz switching frequency)based on silicon carbide(SiC)MOSFET is deeply integrated with fuzzy model predictive control(Fuzzy-MPC).At the hardware level,the switching trajectory and resonance suppression circuit(attenuation resonance peak 18 dB)are optimized,and the total loss is reduced by 23%compared with the traditional silicon-based IGBT.At the algorithm level,the adaptive parameter update mechanism and multi-objective rolling optimization are adopted,and the 5 ms level dynamic power allocation is realized by relying on edge computing.Experiments on 800 V DC microgrid(including 600 kW photovoltaic and 150 A·h energy storage)built based on MATLAB/Simulink hardware-in-the-loop(HIL)platform show that the system shortens the battery charging time from 42 to 28 min(the charging speed is increased by 33%).Through the 78%valley power utilization rate,the power purchase cost of high-priced power grids was significantly reduced,and the levelized electricity price decreased by 10.3%;Under the irradiation fluctuation,the renewable energy consumption rate increases by 10.1%,and the DC bus voltage fluctuation is stable within±10 V when the load step is±30%.The co-design provides an economically feasible and dynamically robust solution for the efficient integration of PV-ESG-EV in the smart grid.展开更多
Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-...Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-tional image-processing pipelines struggle with scalability and robustness,and recent deep learning methods remain sensitive to class imbalance and acquisition variability.This paper introduces TurbineBladeDetNet,a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types.Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability.The model achieves 97.14%accuracy,98.65%precision,and 98.68%recall,yielding a 98.66%F1-score with 0.0110 s inference time.Class-specific analysis shows uniformly high sensitivity and specificity;lightning damage reaches 99.80%for sensitivity,precision,and F1-score,and crack achieves perfect precision and specificity with a 98.94%F1-score.Comparative evaluation against recent wind-turbine inspection approaches indicates higher performance in both accuracy and F1-score.The resulting balance of sensitivity and specificity limits both missed defects and false alarms,supporting reliable deployment in routine unmanned aerial vehicle(UAV)inspection.展开更多
In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along...In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along the primary delivery feeder from the external network.Besides,the giant induction electro-motors as the working horse of industries requires remarkable amounts of reactive power for electro-mechanical energy conversions.To reduce power losses and operating costs of the MG as well as to improve the voltage quality,this study aims at providing an insightful model for optimal placement and sizing of reactive power compensation capacitors in an industrial MG.In the presented model,the objective function considers voltage profile and network power factor improvement at the MG connection point.Also,it realizes power flow equations within which all operational security constraints are considered.Various reactive power compensation strategies including distributed group compensation,centralized compensation at the main substation,and distributed compensation along the primary delivery feeder are scrutinized.A real industrial MG,say as Urmia Petrochemical plant,is considered in numerical validations.The obtained results in each scenario are discussed in depth.As seen,the best performance is obtained when the optimal location and sizing of capacitors are simultaneously determined at the main buses of the industrial plants,at the main substation of the MG,and alongside the primary delivery feeder.In this way,74.81%improvement in power losses reduction,1.3%lower active power import from the main grid,23.5%improvement in power factor,and 37.5%improvement in network voltage deviation summation are seen in this case compared to the base case.展开更多
Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation...Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.展开更多
This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raph...This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.展开更多
Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of mul...Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.展开更多
As a generalization of the successful hidden Markov models,Dynamic Bayesian Networks(DBNs)are a natural basis for the general temporal action interpretation task.This document provides a conditional probabilistic appr...As a generalization of the successful hidden Markov models,Dynamic Bayesian Networks(DBNs)are a natural basis for the general temporal action interpretation task.This document provides a conditional probabilistic approach to analyze the energy availability in electrical distribution networks by using Bayesian networks(BN).Firstly a static BN modelling is presented to show the influence of the switch behaviour on the energy availability.Then,the dynamic behaviour of the switch is cared by switch reliability modelling using DBN which permits to predict the energy availability.The prediction by DBNs discussed in the case study of this paper gives a strong contribution on electrical network supervisory control and it can also be applied to transportation networks.展开更多
Congestion is the prime cause of problems, due to open access of power system. The AC Power Transmission Congestion Distribution factor (PTCDF) is suitable for computing change in any line quantity for a change in MW ...Congestion is the prime cause of problems, due to open access of power system. The AC Power Transmission Congestion Distribution factor (PTCDF) is suitable for computing change in any line quantity for a change in MW bilateral transaction. The proposed PTCDF method is more accurate as compared to the DC power distribution factor. With PTCDF ATC can be calculated. After calculating ATC it is possible to know the valid multiple transaction on power system. With the help of ATC calculations congestion problem can be solved in restructured electrical power network. The paper presents the method for calculating ATC using PTCDF.展开更多
The construction and operation of atmospheric nonthermal plasma jet, ANPJ, are presented in this work as well as the experimental investigations of its electrical parameters, the configuration of plasma jet column and...The construction and operation of atmospheric nonthermal plasma jet, ANPJ, are presented in this work as well as the experimental investigations of its electrical parameters, the configuration of plasma jet column and its temperature. The device is energized by a low-cost Neon power supply of (10 kV, 30 mA, and 20 kHz) and the discharge takes place by using N2 gas with different flow rates from 3 to 25 L/min and input voltage of 6 kV. Diagnostic techniques such as voltage divider, Lissajous figure, image processing and thermometer are used. The electrical characteristics of discharge at different flow rates of N2 gas such as discharge voltage, current, mean power, power efficiency, and mean energy have been studied. The experimental results show that the maximum plasma jet length of 14 mm is detected at flow rate of 12 L/min. The results of plasma jet (heavy particles) temperature along the jet length show that jet plasma has approximately a room temperature at the jet column end. The results of zero flow rate effect on the ANPJ operation show damage in the Teflon insulator and a corrosion in the Aluminum electrodes.展开更多
Because of increased need to tissue and organ transplantation, tissue engineering (TE) researches have significantly increased in recent years in Iran. The present study explored briefly the advances in the TE approac...Because of increased need to tissue and organ transplantation, tissue engineering (TE) researches have significantly increased in recent years in Iran. The present study explored briefly the advances in the TE approaches in Iran. Through comprehensive search, we explored main TE components researches include cell, scaffold, growth factor and bioreactor conducted in Iran. The field of TE and regenerative medicine in Iran dates back to the early part of the 1990 decade and the advent of stem cell researches. During past two decades, Iran was one of leader in stem cell research in Middle East. The next major step in TE was application and fabrication of scaffolds for TE in the early 2000s with focused on engineering bone and nerve tissue. Iranian researchers extensively used natural scaffolds in their studies and hybridized natural polymers and inorganic scaffolds. There are many universities and government research institutes are conducting active research on tissue-engineering technologies. Limitations to TE in Iran include property design and validation of bioreactors. In conclusion, in the last few years, fields of tissue engineering and regenerative medicine such as stem cell technology and scaffolds have progressed in Iran, but one of the biggest challenges for TE is bioreactors researches.展开更多
VFDs （variable frequency drives） are an integral part of many industrial plants and stations. Reliable operation and maintenance of these drives is vital to ensure sustained plant operation and availability. Underst...VFDs （variable frequency drives） are an integral part of many industrial plants and stations. Reliable operation and maintenance of these drives is vital to ensure sustained plant operation and availability. Understanding of the principles of operation of VFD systems as well as knowledge about their required operating environment is necessary for all operating personnel. Many times the operating personnel do not get involved with different technical issues until a complete failure has occurred. Hence, the awareness of the most dominant failure causes has a significant impact on assisting operators to avoid catastrophic failures and tremendous economic losses due to VFD shutdown. Proper plant design, accurate monitoring and data logging, following manufacturer preventive maintenance schedule, and choosing qualified team of operators can be the key to an efficient operation and a long lifetime for any VFD system. In this paper, we have analyzed the electrical and non-electrical causes of VFD failures based on a case study of a typical medium voltage VFD pumping station. Finally, recommendations are given from field analysis and observations.展开更多
The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable energy.Solar ener...The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable energy.Solar energy,among the various renewable sources,is particularly appealing due to its abundant availability.However,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight hours.This study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat transfer.The CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of 10.These specific parameters were found to maximize the heat transfer and temperature reduction efficiency.This finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar panels.Alternative cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.展开更多
The hyperloop idea,which is one of the most ecofriendly,low-carbon emissions,and fossil fuel-efficient modes of transportation,has recently become quite popular.In this study,a double-sided linear induction motor(LIM)...The hyperloop idea,which is one of the most ecofriendly,low-carbon emissions,and fossil fuel-efficient modes of transportation,has recently become quite popular.In this study,a double-sided linear induction motor(LIM)with 500 W of output power,60 N of thrust force and 200 V/38.58 Hz of supply voltage was designed to be used in hyperloop development competition hosted by the scientific and technological research council of turkey(TüB?TAK)rail transportation technologies institute(RUTE).In contrast to the studies in the literature,concentrated winding is preferred instead of distributed winding due to mechanical constraints.The electromagnetic design of LIM,whose mechanical and electrical requirements were determined considering the hyperloop development competition,was carried out by following certain steps.Then,the designed model was simulated and analyzed by finite element method(FEM),and the necessary optimizations have been performed to improve the motor characteristics.By examining the final model,the applicability of the concentrated winding type LIM for hyperloop technology has been investigated.Besides,the effects of primary material,railway material,and mechanical air-gap length on LIM performance were also investigated.In the practical phase of the study,the designed LIM has been prototyped and tested.The validation of the experimental results was achieved through good agreement with the finite element analysis results.展开更多
Global mortality rates are greatly impacted by malignancies of the brain and nervous system.Although,Magnetic Resonance Imaging(MRI)plays a pivotal role in detecting brain tumors;however,manual assessment is time-cons...Global mortality rates are greatly impacted by malignancies of the brain and nervous system.Although,Magnetic Resonance Imaging(MRI)plays a pivotal role in detecting brain tumors;however,manual assessment is time-consuming and susceptible to human error.To address this,we introduce ICA2-SVM,an advanced computational framework integrating Independent Component Analysis Architecture-2(ICA2)and Support Vector Machine(SVM)for automated tumor segmentation and classification.ICA2 is utilized for image preprocessing and optimization,enhancing MRI consistency and contrast.The Fast-MarchingMethod(FMM)is employed to delineate tumor regions,followed by SVM for precise classification.Validation on the Contrast-Enhanced Magnetic Resonance Imaging(CEMRI)dataset demonstrates the superior performance of ICA2-SVM,achieving a Dice Similarity Coefficient(DSC)of 0.974,accuracy of 0.992,specificity of 0.99,and sensitivity of 0.99.Additionally,themodel surpasses existing approaches in computational efficiency,completing analysis within 0.41 s.By integrating state-of-the-art computational techniques,ICA2-SVM advances biomedical imaging,offering a highly accurate and efficient solution for brain tumor detection.Future research aims to incorporate multi-physics modeling and diverse classifiers to further enhance the adaptability and applicability of brain tumor diagnostic systems.展开更多
Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emp...Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.展开更多
A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumor...A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.展开更多
The utilization of hybrid energy systems has necessitated to address the various Power Quality(PQ)concerns in Distributed Generation(DG)networks.Owing to the emergence of DG networks in recent times,it is envisaged fo...The utilization of hybrid energy systems has necessitated to address the various Power Quality(PQ)concerns in Distributed Generation(DG)networks.Owing to the emergence of DG networks in recent times,it is envisaged for every utility⁃grid⁃tied system to generate and utilize harmonic⁃less electric power.Therefore,the present research critically evaluates the operation of a utility⁃grid coordinated DG system and studies its islanding operation under faulted conditions.To achieve this,an Anti⁃Islanding Protection(AIP)scheme is developed which is capable of controlling the frequency and voltage variations.This scheme is operated by a coordinated operation of multivibrators.Their operation continuously traces the pre⁃defined limits of voltage,reactive,and real power,and matches with their reference values to avoid mismatch.It is revealed that,if the mismatched values of real and reactive power exceeded its threshold value of 0.1 p.u.,then the islanding condition is detected.Especially,the proposed system is assessed in two modes:utility⁃grid and islanding modes.In utility⁃grid mode,reactive power compensation is obtained by the control of voltage and frequency signals.However,in islanding mode,the real power requirement of the connected load is obtained with reduced harmonics under unsymmetrical faulted conditions.Incremental Conductance(IC)based Maximum Power Point Tracking(MPPT)technique ensures the extraction of maximum power under varying and stochastically atmospheric conditions.Simulation results reveal that the AIP scheme promptly disconnects the utility grid from the DG network in the minimum time during dynamic variations in frequency and voltage to prevent islanding.It is justified that there is violation of the considered threshold limits even under the faulted condition.The strategy of the switchgear scheme ensures the minimum detection time of the islanding operation.Total Harmonic Distortion(THD)is 0.26%for grid voltage.It validates according to the IEEE⁃1547 standard which stipulates that the THD of grid voltage must be less than 5%.Overall,satisfactory and accurate results are obtained,which are compared with the IEEE⁃1547 standard for validation.展开更多
Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and...Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and estimate the optimal location of Static Synchronous Compensator(STATCOM) by reducing congestion for a deregulated power system.The proposed method is based on the use of Locational Marginal Price(LMP) difference technique and congestion cost.LMPs are obtained as a by-product of Optimal Power Flow(OPF),whereas Congestion Cost(CC) is a function of difference in LMP and power flows.The effiectiveness of this approach is demonstrated by reducing the CC and solution space which can identify the TLs more suitable for placement of STATCOM.Importantly,total real power loss,reactive power loss and total CC are the three main objective functions in this optimization process.The process is implemented by developing an IEEE-69 bus test system which verifies and validates the effectiveness of proposed optimization technique.Additionally,a comparative analysis is enumerated by implementing two optimization techniques:Flower Pollination Algorithm(FPA) and Particle Swarm Optimization(PSO).The comparative analysis is sufficient to demonstrate the superiority of FPA technique over PSO technique in estimating an optimal placement of a STATCOM.The results from the load-flow analysis illustrate the reduction in CC,total real and reactive power loss using FPA technique compared to PSO technique.Overall,satisfactory results are obtained without using complex calculations which verify the effectiveness of optimization techniques.展开更多
In this paper, a bearing-based three-dimensional self-localization and distributed circumnavigation with connectivity preservation and collision avoidance are investigated for a group of quadrotor-type unmanned aerial...In this paper, a bearing-based three-dimensional self-localization and distributed circumnavigation with connectivity preservation and collision avoidance are investigated for a group of quadrotor-type unmanned aerial vehicles (UAVs). A leader–follower structure is adopted, wherein the leader moves with reference dynamics (a target). Different from the existing approaches that necessitate full knowledge of the time-varying reference trajectory, in this paper, it is assumed that only some vehicles (at least one) have access to the bearing relative to the target, and all other vehicles are equipped with sensors capable of measuring the bearings relative to neighboring vehicles. In this paper, a consensus estimator is proposed to estimate the global position for each vehicle using relative bearing measurements and an estimate of neighboring vehicles received from a direct communication network. Then, a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the distributed vector field approach to ensure UAV formation orbiting around the moving target while avoiding obstacles and maintaining network links within available communication ranges. In contrast to the classical RISE control rule, a \(\tanh (\cdot )\) function is used instead of the \(\text {sgn}(\cdot )\) function to further decrease the high-gain feedback and to obtain a smoother control signal. Furthermore, by using the localized radial basis function (RBF) neural networks (NNs) in a cooperative way, deterministic learning theory is employed to accurately identify/learn model uncertainties resulting from the attitude dynamics. The convergence of the entire closed-loop system is illustrated using the Lyapunov theory and is shown to be uniformly ultimately bounded. Finally, numerical simulations show the effectiveness of the proposed approach.展开更多
This research investigates the design and optimization of a photovoltaic(PV)water pumping system to address seasonal water demands across five locations with varying elevation heads.The systemdraws water froma deep we...This research investigates the design and optimization of a photovoltaic(PV)water pumping system to address seasonal water demands across five locations with varying elevation heads.The systemdraws water froma deep well with a static water level of 30mand a dynamic level of 50m,serving agricultural and livestock needs.The objective of this study is to accurately size a PV system that balances energy generation and demand while minimizing grid dependency.Meanwhile,the study presents a comprehensivemethodology to calculate flowrates,pumping power,daily energy consumption,and system capacity.Therefore,the PV system rating,energy output,and economic performance were evaluated using metrics such as discounted payback period(DPP),net present value(NPV),and sensitivity analysis.The results show that a 2.74 kWp PV system is optimal,producing 4767 kWh/year to meet the system’s annual energy demand of 4686 kWh.In summer,energy demand peaks at 1532.7 kWh,while in winter,it drops to 692.1 kWh.Meanwhile,flow rates range from 11.71 m^(3)/h at 57 m head to 10.49 m^(3)/h at 70 m head,demonstrating the system’s adaptability to diverse hydraulic conditions.Economic analysis reveals that at a 5%interest rate and an electricity price of$0.15/kWh,the NPV is$6981.82 with a DPP of 3.76 years.However,a 30%increase in electricity prices improves the NPV to$10,005.18 and shortens the DPP to 2.76 years,whereas a 20%interest rate reduces the NPV to$1038.79 and extends the DPP to 6.08 years.Nevertheless,the annual PV energy generation exceeds total energy demand by 81 kWh,reducing grid dependency and lowering electricity costs.Additionally,the PV system avoids approximately 3956.6 kg of CO_(2) emissions annually,underscoring its environmental benefits over traditional pumping systems.As a result,this study highlights the economic and environmental viability of PV-powered water pumping systems,offering actionable insights for sustainable energy solutions in agriculture.展开更多
基金Jiangsu Provincial College Student Innovation and Entrepreneurship Program(Grant No.SJCX25_2184)—“Multi-energy Complementary Optimization and Vehicle-Storage Bidirectional Interaction Technology Driven by Novel 5E Framework”(Principal Investigator:Yuan-Yuan ShiFunding Agency:Jiangsu Provincial Education Department)+3 种基金Huaian Natural Science Research Project(Grant No.HAB2024046)—“Optimal Control of Flexible Cold-Heat-Power Integrated System with Source-Grid-Load-Storage Coordination”(Principal Investigator:Jie JiFunding Agency:Huaian Science and Technology Bureau)Huaiyin Institute of TechnologyUniversity-funded Project(GrantNo.HGYK202511)—“Data-driven CooperativeOptimization Dispatch for Source-Grid-Load Systems”(Principal Investigator:Chu-Tong ZhangFunding Agency:Huaiyin Institute of Technology).
文摘In order to solve the problems of slow dynamic response and difficult multi-source coordination of solar electric vehicle charging stations under intermittent renewable energy,this paper proposes a hardware-algorithm co-design framework:the T-type three-level bidirectional converter(100 kHz switching frequency)based on silicon carbide(SiC)MOSFET is deeply integrated with fuzzy model predictive control(Fuzzy-MPC).At the hardware level,the switching trajectory and resonance suppression circuit(attenuation resonance peak 18 dB)are optimized,and the total loss is reduced by 23%compared with the traditional silicon-based IGBT.At the algorithm level,the adaptive parameter update mechanism and multi-objective rolling optimization are adopted,and the 5 ms level dynamic power allocation is realized by relying on edge computing.Experiments on 800 V DC microgrid(including 600 kW photovoltaic and 150 A·h energy storage)built based on MATLAB/Simulink hardware-in-the-loop(HIL)platform show that the system shortens the battery charging time from 42 to 28 min(the charging speed is increased by 33%).Through the 78%valley power utilization rate,the power purchase cost of high-priced power grids was significantly reduced,and the levelized electricity price decreased by 10.3%;Under the irradiation fluctuation,the renewable energy consumption rate increases by 10.1%,and the DC bus voltage fluctuation is stable within±10 V when the load step is±30%.The co-design provides an economically feasible and dynamically robust solution for the efficient integration of PV-ESG-EV in the smart grid.
文摘Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-tional image-processing pipelines struggle with scalability and robustness,and recent deep learning methods remain sensitive to class imbalance and acquisition variability.This paper introduces TurbineBladeDetNet,a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types.Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability.The model achieves 97.14%accuracy,98.65%precision,and 98.68%recall,yielding a 98.66%F1-score with 0.0110 s inference time.Class-specific analysis shows uniformly high sensitivity and specificity;lightning damage reaches 99.80%for sensitivity,precision,and F1-score,and crack achieves perfect precision and specificity with a 98.94%F1-score.Comparative evaluation against recent wind-turbine inspection approaches indicates higher performance in both accuracy and F1-score.The resulting balance of sensitivity and specificity limits both missed defects and false alarms,supporting reliable deployment in routine unmanned aerial vehicle(UAV)inspection.
文摘In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along the primary delivery feeder from the external network.Besides,the giant induction electro-motors as the working horse of industries requires remarkable amounts of reactive power for electro-mechanical energy conversions.To reduce power losses and operating costs of the MG as well as to improve the voltage quality,this study aims at providing an insightful model for optimal placement and sizing of reactive power compensation capacitors in an industrial MG.In the presented model,the objective function considers voltage profile and network power factor improvement at the MG connection point.Also,it realizes power flow equations within which all operational security constraints are considered.Various reactive power compensation strategies including distributed group compensation,centralized compensation at the main substation,and distributed compensation along the primary delivery feeder are scrutinized.A real industrial MG,say as Urmia Petrochemical plant,is considered in numerical validations.The obtained results in each scenario are discussed in depth.As seen,the best performance is obtained when the optimal location and sizing of capacitors are simultaneously determined at the main buses of the industrial plants,at the main substation of the MG,and alongside the primary delivery feeder.In this way,74.81%improvement in power losses reduction,1.3%lower active power import from the main grid,23.5%improvement in power factor,and 37.5%improvement in network voltage deviation summation are seen in this case compared to the base case.
基金supported by Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/540/46.
文摘Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.
基金funded by the Deanship of Scientific Research and Libraries,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No.(RPFAP-82-1445)。
文摘This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.
基金funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia(Grant No.13292-psu-2023-PSNU-R-3-1-EF-).
文摘Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.
文摘As a generalization of the successful hidden Markov models,Dynamic Bayesian Networks(DBNs)are a natural basis for the general temporal action interpretation task.This document provides a conditional probabilistic approach to analyze the energy availability in electrical distribution networks by using Bayesian networks(BN).Firstly a static BN modelling is presented to show the influence of the switch behaviour on the energy availability.Then,the dynamic behaviour of the switch is cared by switch reliability modelling using DBN which permits to predict the energy availability.The prediction by DBNs discussed in the case study of this paper gives a strong contribution on electrical network supervisory control and it can also be applied to transportation networks.
文摘Congestion is the prime cause of problems, due to open access of power system. The AC Power Transmission Congestion Distribution factor (PTCDF) is suitable for computing change in any line quantity for a change in MW bilateral transaction. The proposed PTCDF method is more accurate as compared to the DC power distribution factor. With PTCDF ATC can be calculated. After calculating ATC it is possible to know the valid multiple transaction on power system. With the help of ATC calculations congestion problem can be solved in restructured electrical power network. The paper presents the method for calculating ATC using PTCDF.
文摘The construction and operation of atmospheric nonthermal plasma jet, ANPJ, are presented in this work as well as the experimental investigations of its electrical parameters, the configuration of plasma jet column and its temperature. The device is energized by a low-cost Neon power supply of (10 kV, 30 mA, and 20 kHz) and the discharge takes place by using N2 gas with different flow rates from 3 to 25 L/min and input voltage of 6 kV. Diagnostic techniques such as voltage divider, Lissajous figure, image processing and thermometer are used. The electrical characteristics of discharge at different flow rates of N2 gas such as discharge voltage, current, mean power, power efficiency, and mean energy have been studied. The experimental results show that the maximum plasma jet length of 14 mm is detected at flow rate of 12 L/min. The results of plasma jet (heavy particles) temperature along the jet length show that jet plasma has approximately a room temperature at the jet column end. The results of zero flow rate effect on the ANPJ operation show damage in the Teflon insulator and a corrosion in the Aluminum electrodes.
文摘Because of increased need to tissue and organ transplantation, tissue engineering (TE) researches have significantly increased in recent years in Iran. The present study explored briefly the advances in the TE approaches in Iran. Through comprehensive search, we explored main TE components researches include cell, scaffold, growth factor and bioreactor conducted in Iran. The field of TE and regenerative medicine in Iran dates back to the early part of the 1990 decade and the advent of stem cell researches. During past two decades, Iran was one of leader in stem cell research in Middle East. The next major step in TE was application and fabrication of scaffolds for TE in the early 2000s with focused on engineering bone and nerve tissue. Iranian researchers extensively used natural scaffolds in their studies and hybridized natural polymers and inorganic scaffolds. There are many universities and government research institutes are conducting active research on tissue-engineering technologies. Limitations to TE in Iran include property design and validation of bioreactors. In conclusion, in the last few years, fields of tissue engineering and regenerative medicine such as stem cell technology and scaffolds have progressed in Iran, but one of the biggest challenges for TE is bioreactors researches.
文摘VFDs （variable frequency drives） are an integral part of many industrial plants and stations. Reliable operation and maintenance of these drives is vital to ensure sustained plant operation and availability. Understanding of the principles of operation of VFD systems as well as knowledge about their required operating environment is necessary for all operating personnel. Many times the operating personnel do not get involved with different technical issues until a complete failure has occurred. Hence, the awareness of the most dominant failure causes has a significant impact on assisting operators to avoid catastrophic failures and tremendous economic losses due to VFD shutdown. Proper plant design, accurate monitoring and data logging, following manufacturer preventive maintenance schedule, and choosing qualified team of operators can be the key to an efficient operation and a long lifetime for any VFD system. In this paper, we have analyzed the electrical and non-electrical causes of VFD failures based on a case study of a typical medium voltage VFD pumping station. Finally, recommendations are given from field analysis and observations.
文摘The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable energy.Solar energy,among the various renewable sources,is particularly appealing due to its abundant availability.However,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight hours.This study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat transfer.The CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of 10.These specific parameters were found to maximize the heat transfer and temperature reduction efficiency.This finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar panels.Alternative cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.
基金the Istanbul Technical University Scientific Research Projects Unit with grant number MGA-2022-43948。
文摘The hyperloop idea,which is one of the most ecofriendly,low-carbon emissions,and fossil fuel-efficient modes of transportation,has recently become quite popular.In this study,a double-sided linear induction motor(LIM)with 500 W of output power,60 N of thrust force and 200 V/38.58 Hz of supply voltage was designed to be used in hyperloop development competition hosted by the scientific and technological research council of turkey(TüB?TAK)rail transportation technologies institute(RUTE).In contrast to the studies in the literature,concentrated winding is preferred instead of distributed winding due to mechanical constraints.The electromagnetic design of LIM,whose mechanical and electrical requirements were determined considering the hyperloop development competition,was carried out by following certain steps.Then,the designed model was simulated and analyzed by finite element method(FEM),and the necessary optimizations have been performed to improve the motor characteristics.By examining the final model,the applicability of the concentrated winding type LIM for hyperloop technology has been investigated.Besides,the effects of primary material,railway material,and mechanical air-gap length on LIM performance were also investigated.In the practical phase of the study,the designed LIM has been prototyped and tested.The validation of the experimental results was achieved through good agreement with the finite element analysis results.
基金supported by the Deanship of Graduate Studies and Scientific Research at Najran University through funding code NU/GP/MRC/13/771-1.
文摘Global mortality rates are greatly impacted by malignancies of the brain and nervous system.Although,Magnetic Resonance Imaging(MRI)plays a pivotal role in detecting brain tumors;however,manual assessment is time-consuming and susceptible to human error.To address this,we introduce ICA2-SVM,an advanced computational framework integrating Independent Component Analysis Architecture-2(ICA2)and Support Vector Machine(SVM)for automated tumor segmentation and classification.ICA2 is utilized for image preprocessing and optimization,enhancing MRI consistency and contrast.The Fast-MarchingMethod(FMM)is employed to delineate tumor regions,followed by SVM for precise classification.Validation on the Contrast-Enhanced Magnetic Resonance Imaging(CEMRI)dataset demonstrates the superior performance of ICA2-SVM,achieving a Dice Similarity Coefficient(DSC)of 0.974,accuracy of 0.992,specificity of 0.99,and sensitivity of 0.99.Additionally,themodel surpasses existing approaches in computational efficiency,completing analysis within 0.41 s.By integrating state-of-the-art computational techniques,ICA2-SVM advances biomedical imaging,offering a highly accurate and efficient solution for brain tumor detection.Future research aims to incorporate multi-physics modeling and diverse classifiers to further enhance the adaptability and applicability of brain tumor diagnostic systems.
文摘Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:1055-829-2024).
文摘A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.
文摘The utilization of hybrid energy systems has necessitated to address the various Power Quality(PQ)concerns in Distributed Generation(DG)networks.Owing to the emergence of DG networks in recent times,it is envisaged for every utility⁃grid⁃tied system to generate and utilize harmonic⁃less electric power.Therefore,the present research critically evaluates the operation of a utility⁃grid coordinated DG system and studies its islanding operation under faulted conditions.To achieve this,an Anti⁃Islanding Protection(AIP)scheme is developed which is capable of controlling the frequency and voltage variations.This scheme is operated by a coordinated operation of multivibrators.Their operation continuously traces the pre⁃defined limits of voltage,reactive,and real power,and matches with their reference values to avoid mismatch.It is revealed that,if the mismatched values of real and reactive power exceeded its threshold value of 0.1 p.u.,then the islanding condition is detected.Especially,the proposed system is assessed in two modes:utility⁃grid and islanding modes.In utility⁃grid mode,reactive power compensation is obtained by the control of voltage and frequency signals.However,in islanding mode,the real power requirement of the connected load is obtained with reduced harmonics under unsymmetrical faulted conditions.Incremental Conductance(IC)based Maximum Power Point Tracking(MPPT)technique ensures the extraction of maximum power under varying and stochastically atmospheric conditions.Simulation results reveal that the AIP scheme promptly disconnects the utility grid from the DG network in the minimum time during dynamic variations in frequency and voltage to prevent islanding.It is justified that there is violation of the considered threshold limits even under the faulted condition.The strategy of the switchgear scheme ensures the minimum detection time of the islanding operation.Total Harmonic Distortion(THD)is 0.26%for grid voltage.It validates according to the IEEE⁃1547 standard which stipulates that the THD of grid voltage must be less than 5%.Overall,satisfactory and accurate results are obtained,which are compared with the IEEE⁃1547 standard for validation.
文摘Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and estimate the optimal location of Static Synchronous Compensator(STATCOM) by reducing congestion for a deregulated power system.The proposed method is based on the use of Locational Marginal Price(LMP) difference technique and congestion cost.LMPs are obtained as a by-product of Optimal Power Flow(OPF),whereas Congestion Cost(CC) is a function of difference in LMP and power flows.The effiectiveness of this approach is demonstrated by reducing the CC and solution space which can identify the TLs more suitable for placement of STATCOM.Importantly,total real power loss,reactive power loss and total CC are the three main objective functions in this optimization process.The process is implemented by developing an IEEE-69 bus test system which verifies and validates the effectiveness of proposed optimization technique.Additionally,a comparative analysis is enumerated by implementing two optimization techniques:Flower Pollination Algorithm(FPA) and Particle Swarm Optimization(PSO).The comparative analysis is sufficient to demonstrate the superiority of FPA technique over PSO technique in estimating an optimal placement of a STATCOM.The results from the load-flow analysis illustrate the reduction in CC,total real and reactive power loss using FPA technique compared to PSO technique.Overall,satisfactory results are obtained without using complex calculations which verify the effectiveness of optimization techniques.
文摘In this paper, a bearing-based three-dimensional self-localization and distributed circumnavigation with connectivity preservation and collision avoidance are investigated for a group of quadrotor-type unmanned aerial vehicles (UAVs). A leader–follower structure is adopted, wherein the leader moves with reference dynamics (a target). Different from the existing approaches that necessitate full knowledge of the time-varying reference trajectory, in this paper, it is assumed that only some vehicles (at least one) have access to the bearing relative to the target, and all other vehicles are equipped with sensors capable of measuring the bearings relative to neighboring vehicles. In this paper, a consensus estimator is proposed to estimate the global position for each vehicle using relative bearing measurements and an estimate of neighboring vehicles received from a direct communication network. Then, a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the distributed vector field approach to ensure UAV formation orbiting around the moving target while avoiding obstacles and maintaining network links within available communication ranges. In contrast to the classical RISE control rule, a \(\tanh (\cdot )\) function is used instead of the \(\text {sgn}(\cdot )\) function to further decrease the high-gain feedback and to obtain a smoother control signal. Furthermore, by using the localized radial basis function (RBF) neural networks (NNs) in a cooperative way, deterministic learning theory is employed to accurately identify/learn model uncertainties resulting from the attitude dynamics. The convergence of the entire closed-loop system is illustrated using the Lyapunov theory and is shown to be uniformly ultimately bounded. Finally, numerical simulations show the effectiveness of the proposed approach.
文摘This research investigates the design and optimization of a photovoltaic(PV)water pumping system to address seasonal water demands across five locations with varying elevation heads.The systemdraws water froma deep well with a static water level of 30mand a dynamic level of 50m,serving agricultural and livestock needs.The objective of this study is to accurately size a PV system that balances energy generation and demand while minimizing grid dependency.Meanwhile,the study presents a comprehensivemethodology to calculate flowrates,pumping power,daily energy consumption,and system capacity.Therefore,the PV system rating,energy output,and economic performance were evaluated using metrics such as discounted payback period(DPP),net present value(NPV),and sensitivity analysis.The results show that a 2.74 kWp PV system is optimal,producing 4767 kWh/year to meet the system’s annual energy demand of 4686 kWh.In summer,energy demand peaks at 1532.7 kWh,while in winter,it drops to 692.1 kWh.Meanwhile,flow rates range from 11.71 m^(3)/h at 57 m head to 10.49 m^(3)/h at 70 m head,demonstrating the system’s adaptability to diverse hydraulic conditions.Economic analysis reveals that at a 5%interest rate and an electricity price of$0.15/kWh,the NPV is$6981.82 with a DPP of 3.76 years.However,a 30%increase in electricity prices improves the NPV to$10,005.18 and shortens the DPP to 2.76 years,whereas a 20%interest rate reduces the NPV to$1038.79 and extends the DPP to 6.08 years.Nevertheless,the annual PV energy generation exceeds total energy demand by 81 kWh,reducing grid dependency and lowering electricity costs.Additionally,the PV system avoids approximately 3956.6 kg of CO_(2) emissions annually,underscoring its environmental benefits over traditional pumping systems.As a result,this study highlights the economic and environmental viability of PV-powered water pumping systems,offering actionable insights for sustainable energy solutions in agriculture.
