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Load frequency control in deregulated power system with wind integrated system using fuzzy controller

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Frontiers in Energy 2013年第2期7卷 245-254页

作者： Yajvender Pal verma Ashwani kumar Department of Electrical & Electronics Engineering UIET PanjabUniversity Chandigarh 160014 India Department of Electrical Engineering National Institute of TechnologyKurukshetra Haryana 132119 India

This paper presents the analysis of load frequency control （LFC） of a deregulated two-area hydro-thermal power system using fuzzy logic controller, with doubly fed induction generators （DFIGs） integrated into both the control areas. The deregulation of power sector has led to the formation of new companies for generation, transmission and distribution of power. The conventional two-area power system is modified to study the effects of the bilateral contracts of companies on the system dynamics. Deregulation creates highly competitive and distributed control environment, and the LFC becomes even more challenging when wind generators are also integrated into the system. The overall inertia of the system reduces, as the wind unit does not provide inertia and isolates from the grid during disturbances. The DFIGs integrated provide inertial support to the system through modified inertial control scheme, and arrests the initial fall in frequency after disturbance. The inertial control responds to frequency deviations, which takes out the kinetic energy of the wind turbine for improving the frequency response of the system. To enhance the participation of the doubly fed induction generator （DFIG） in the frequency control, optimal values of the speed control parameters of the DFIG-based wind turbine have been obtained using integral square error （ISE） technique. The dynamics of the system have been obtained for a small load perturbation, and for contract violation using fuzzy controller.

关键词： frequency regulation fuzzy controller deregulated power system doubly fed induction generator(DFIG) bilateral contract

Multifunctional characteristics of 3D printed polymer nanocomposites under monotonic and cyclic compression

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Defence Technology（防务技术） 2023年第12期30卷 13-22页

作者： Pawan verma Jabir Ubaid Fahad Alam Suleyman Deveci S.kumar Department of Mechanical Engineering Khalifa University of Science and TechnologyMasdar InstituteMasdar CityP.O.Box 54224Abu DhabiUnited Arab Emirates Borouge Pte.Ltd. Innovation CentreSas Al Nakhl6951Abu DhabiUnited Arab Emirates James Watt School of Engineering University of GlasgowGlasgowG128QQUK

This study presents the multifunctional characteristics of multi-walled carbon nanotube(MWCNT)/polypropylene random copolymer(PPR) composites enabled via fused filament fabrication(FFF) under monotonic and quasi-static cyclic compression. Utilizing in-house MWCNT-engineered PPR filament feedstocks, both bulk and cellular composites were realized. The morphological features of nanocomposites were examined via scanning electron microscopy, which reveals that MWCNTs are uniformly dispersed. The uniformly dispersed MWCNTs forms an electrically conductive network within the PPR matrix, and the resulting nanocomposite shows good electrical conductivity(~10^(-1)S/cm), improved mechanical performance(modulus increases by 125% and compressive strength increases by 25% for 8 wt% MWCNT loading) and pronounced piezoresistive response(gauge factor of 27.9-8.5 for bulk samples)under compression. The influence of strain rate on the piezoresistive response of bulk samples(4 wt% of MWCNT) under compression was also measured. Under repeated cyclic compression(2% constant strain amplitude), the nanocomposite exhibited stable piezoresistive performance up to 100 cycles. The piezoresistive response under repeated cyclic loading with increasing strain amplitude of was also *** gauge factor of BCC and FCC cellular composites(4 wt% of MWCNT) with a relative density of 30%was observed to be 46.4 and 30.2 respectively, under compression. The higher sensitivity of the BCC plate-lattice could be attributed to its higher degree of stretching-dominated deformation behavior than the FCC plate-lattice, which exhibits bending-dominated behavior. The 3D printed cellular PPR/MWCNT composites structures were found to show excellent piezoresistive self-sensing characteristics and open new avenues for in situ structural health monitoring in various applications.

关键词： Carbon nanotubes Nanoengineered polymer composites 3D printing Piezoresistive self-sensing Lattice structures

Dynamic contribution of variable-speed wind energy conversion system in system frequency regulation

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Frontiers in Energy 2012年第2期6卷 184-192页

作者： Yajvender Pal verma Ashwani kumar Department of Electrical&Electronics Engineering UIETPanjab UniversityChandigarh 160014India Department of Electrical Engineering National Institute of TechnologyKurukshetraHaryana 132119India

Frequency regulation in a generation mix having large wind power penetration is a critical issue, as wind units isolate from the grid during disturbances with advanced power electronics controllers and reduce equivalent system inertia. Thus, it is important that wind turbines also contribute to system frequency control. This paper examines the dynamic contribution of doubly fed induction generator （DFIG）-based wind turbine in system frequency regulation. The modified inertial support scheme is proposed which helps the DFIG to provide the short term transient active power support to the grid during transients and arrests the fall in frequency. The frequency deviation is considered by the controller to provide the inertial control. An additional reference power output is used which helps the DFIG to release kinetic energy stored in rotating masses of the turbine. The optimal speed control parameters have been used for the DFIG to increases its participation in frequency control. The simulations carried out in a two-area interconnected power system demonstrate the contribution of the DFIG in load frequency control.

关键词： doubly fed induction generator (DFIG) load frequency control inertial control wind energy conversion system (WECS)

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

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Computers, Materials & Continua 2020年第10期65卷 743-760页

作者： Peipeng Yu Zhihua Xia Jianwei Fei sunil kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareJiangsu Engineering Center of Network MonitoringJiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologyNanjing University of Information Science&TechnologyNanjing210044China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Coronaviruses are a well-known family of viruses that can infect humans or ***,the new coronavirus(COVID-19)has spread *** countries in the world are working hard to control the coronavirus ***,many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system,which leads to the mass spread of *** a powerful tool,artificial intelligence(AI)has been successfully applied to solve various complex problems ranging from big data analysis to computer *** the process of epidemic control,many algorithms are proposed to solve problems in various fields of medical treatment,which is able to reduce the workload of the medical *** to excellent learning ability,AI has played an important role in drug development,epidemic forecast,and clinical *** research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.

关键词： Artificial intelligence COVID-19 medical applications clinical diagnosis

A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

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Computers, Materials & Continua 2022年第7期72卷 1123-1137页

作者： Xiaorui Zhang Jie Zhou Wei Sun sunil kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044 China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

The key to preventing the COVID-19 is to diagnose patients quickly and *** have shown that using Convolutional Neural Networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely COVID-19 ***,personal privacy issues,public chest CT data sets are relatively few,which has limited CNN’s application to COVID-19 ***,many CNNs have complex structures and massive *** if equipped with the dedicated Graphics Processing Unit(GPU)for acceleration,it still takes a long time,which is not conductive to widespread *** solve above problems,this paper proposes a lightweight CNN classification model based on transfer *** the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing *** order to alleviate the problem of model overfitting caused by insufficient data set,transfer learning is used to train the *** study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network,and then retrain the model based on the CT image data set provided by *** results on a computer equipped only with the Central Processing Unit(CPU)show that it consumes only 1.06 s on average to diagnose a chest CT *** to other lightweight models,the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters,which can be easily applied to computers without GPU ***:***/ZhouJie-520/paper-codes.

关键词： Convolutional neural networks chest computed tomography image COVID-19 transfer learning mobileNetv2

Metallurgical Behaviour and Carbon Diffusion in Buttering Deposits Prepared With and Without Buffer Layers

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Acta Metallurgica Sinica(English Letters) 2017年第2期30卷 120-132页

作者： Dinesh W.Rathod sunil Pandey Sivanandam Aravindan Pavan kumar Singh Department of Mechanical Engineering Indian Institute of Technology Delhi Hauz-Khas New Delhi 110016 India School of MACE University of Manchester Manchester M13 9PL UK Bhabha Atomic Research Centre Mumbai 400085 India

Use of a buttering deposit on ferritic steel in dissimilar metal weld（DMW） joint is a common practice in nuclear plants to connect pressure vessel components（ferritic steel） to pipelines（austenitic stainless steel）.Carbon migration and metallurgical changes near fusion interface（ferritic steel–austenitic stainless steel） lead to a steeper gradient in material properties,and minimizing this gradient is the major challenge in the manufacturing of DMW *** 82 is often deposited on ferritic steel material as buttering to reducing the gradient of physical and attaining material *** 82/182 fillers are used to minimize the carbon migration,but the results are not truly *** this paper,Ni–Fe alloy（chromium-free） has been used as the intermediate buffer layer in the weld buttering deposit to address the issue of carbon migration and subsequent metallurgical *** weld pads with and without buffer layers of Ni–Fe alloy have been investigated and compared in detail for metallurgical properties and carbon ***–Fe buffer layer can significantly control the carbon migration which resists the metallurgical *** showed the better results in postweld heat treatment and thermally aged *** buttering deposit with Ni–Fe buffer layer could be the better choice for DMW joints requirements.

关键词： Dissimilar welds Buttering Carbon diffusion Buffer layer Metallurgical properties

Microsponges for dermatological applications:Perspectives and challenges

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Asian Journal of Pharmaceutical Sciences 2020年第3期15卷 273-291页

作者： Sheefali Mahant sunil kumar Sanju Nanda Rekha Rao Department of Pharmaceutical Sciences Maharshi Dayanand UniversityRohtak 124001India Department of Pharmaceutical Sciences Guru Jambheshwar University of Science and TechnologyHisar 125001India

Dermatological disorders have a huge psychosocial impact, causing significant impairment of patient’s life. Topical therapy plays a pivotal role in management of such *** topical delivery systems result in overmedication/undermedication, leading to adverse effects and reduction in therapeutic efficacy. Consequently, researchers have been striving towards the development of alternative delivery systems for dermatological applications. In the last decade, microsponges emerged as an attractive option for topical delivery. Their characteristic particle size offers enhanced benefits, making them superior to the contemporary microcarriers. The present review furnishes a comprehensive account of state of the art, important factors affecting the performance and mechanism of drug release from topically applied microsponges, along with characterization techniques. Further, a list of marketed products and their applications for common dermatological disorders has been presented. All in all, this paper is an attempt to lay a bibliographic foundation for researchers working in this field and foster further investigations in this arena.

关键词： Skin Polymers Drug release Dermatology Safety aspects Stability

Image Emotion Classification Network Based on Multilayer Attentional Interaction,Adaptive Feature Aggregation

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Computers, Materials & Continua 2023年第5期75卷 4273-4291页

作者： Xiaorui Zhang Chunlin Yuan Wei Sun sunil kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Adani University AhmedabadGujaratIndia

The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the *** have shown that certain local regions are more likely to inspire an emotional response than the whole ***,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the ***,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature *** perform more accurate emotional region prediction,this study designs a multilayer attentional interaction *** module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention *** layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal *** complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation *** module uses global average pooling to compress spatial information and connect channel information from all ***,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each ***,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost *** reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small *** experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods

关键词： Attentionmechanism emotional region prediction image emotion classification transfer learning

Nonlinear Time Series Analysis of Pathogenesis of COVID-19 Pandemic Spreadin Saudi Arabia

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Computers, Materials & Continua 2021年第1期66卷 805-825页

作者： sunil kumar Sharma Shivam Bhardwaj Rashmi Bhardwaj Majed Alowaidi College of Computer and Information Sciences Majmaah UniversityMajmaah11952Saudi Arabia Maharaja Agrasen Institute of Technology Guru Gobind Singh Indraprastha UniversityDelhi110086India Non-Linear Dynamics Research Laboratory GGS Indraprastha UniversityDelhi110078India

This article discusses short–term forecasting of the novel Corona Virus(COVID-19)data for infected and recovered cases using the ARIMA method for Saudi *** COVID-19 data was obtained from the Worldometer and MOH(Ministry of Health,Saudi Arabia).The data was analyzed for the period from March 2,2020(the first case reported)to June 15,*** ARIMA(2,1,0),we obtained the short forecast up to July 02,*** statistical parameters were tested for the goodness of fit to evaluate the forecasting *** results show that ARIMA(2,1,0)gave a better forecast for the data *** 19 data followed quadratic behavior,and in the long run,it spreads with a high *** is concluded that COVID-19 will follow secondary shock waves,and it is strongly advisable to maintain social distancing with all safety measures as the pandemic situation is not in control.

关键词： COVID-19 short-term forecast ARIMA GIS