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Provision of Ramp-rate Limitation as Ancillary Service from Distribution to Transmission System:Definitions and Methodologies for Control and Sizing of Central Battery Energy Storage System

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1507-1518页

作者： Spyros I.Gkavanoudis Kyriaki-Nefeli D.Malamaki Eleftherios O.Kontis Aditya Shekhar Umer Mushtaq sagar bandi venu Charis S.Demoulias Department of Electrical&Computer Engineering Aristotle University of ThessalonikiThessalonikiGreece Electrical Engineering Mathematics&Computer Science DepartmentDelft University of TechnologyDelftNetherlands FENECON Gmbh DeggendorfGermany

The variability of the output power of distributed renewable energy sources(DRESs)that originate from the fastchanging climatic conditions can negatively affect the grid ***,grid operators have incorporated ramp-rate limitations(RRLs)for the injected DRES power in the grid *** the DRES penetration levels increase,the mitigation of high-power ramps is no longer considered as a system support function but rather an ancillary service(AS).Energy storage systems(ESSs)coordinated by RR control algorithms are often applied to mitigate these power ***,no unified definition of active power ramps,which is essential to treat the RRL as AS,currently *** paper assesses the various definitions for ramp-rate RR and proposes RRL method control for a central battery ESS(BESS)in distribution systems(DSs).The ultimate objective is to restrain high-power ramps at the distribution transformer level so that RRL can be traded as AS to the upstream transmission system(TS).The proposed control is based on the direct control of theΔP/Δt,which means that the control parameters are directly correlated with the RR requirements included in the grid *** addition,a novel method for restoring the state of charge(So C)within a specific range following a high ramp-up/down event is ***,a parametric method for estimating the sizing of central BESSs(BESS sizing for short)is *** BESS sizing is determined by considering the RR requirements,the DRES units,and the load mix of the examined *** BESS sizing is directly related to the constant RR achieved using the proposed ***,the proposed methodologies are validated through simulations in MATLAB/Simulink and laboratory tests in a commercially available BESS.

关键词： Battery energy storage system distributed renewable energy resource sizing distribution system transmission system ramp-rate limitation state of charge

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Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

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Computers, Materials & Continua 2021年第3期66卷 2939-2955页

作者： Mangena venu Madhavan Dang Ngoc Hoang Thanh Aditya Khamparia sagar Pande RahulMalik Deepak Gupta School of Computer Science Engineering Lovely Professional UniversityPunjab144411India University of Economics Ho Chi Minh City Ho Chi Minh City70000Vietnam Maharaja Agrasen Institute of Technology 110086India

Disease recognition in plants is one of the essential problems in agricultural image *** article focuses on designing a framework that can recognize and classify diseases on pomegranate plants *** framework utilizes image processing techniques such as image acquisition,image resizing,image enhancement,image segmentation,ROI extraction(region of interest),and feature *** image dataset related to pomegranate leaf disease is utilized to implement the framework,divided into a training set and a test *** the implementation process,techniques such as image enhancement and image segmentation are primarily used for identifying ROI and *** image classification will then be implemented by combining a supervised learning model with a support vector *** proposed framework is developed based on MATLAB with a graphical user *** to the experimental results,the proposed framework can achieve 98.39%accuracy for classifying diseased and healthy ***,the framework can achieve an accuracy of 98.07%for classifying diseases on pomegranate leaves.

关键词： Image enhancement image segmentation image processing for agriculture K-means multi-class support vector machine

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