LEADER 03872nam 2200865z- 450 001 9910566463203321 005 20220506 035 $a(CKB)5680000000037749 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/80971 035 $a(oapen)doab80971 035 $a(EXLCZ)995680000000037749 100 $a20202205d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aIntegration and Control of Distributed Renewable Energy Resources 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (148 p.) 311 08$a3-0365-3689-2 311 08$a3-0365-3690-6 330 $aThe deployment of distributed renewable energy resources (DRERs) has accelerated globally due to environmental concerns and an increasing demand for electricity. DRERs are considered to be solutions to some of the current challenges related to power grids, such as reliability, resilience, efficiency, and flexibility. However, there are still several technical and non-technical challenges regarding the deployment of distributed renewable energy resources. Technical concerns associated with the integration and control of DRERs include, but are not limited, to optimal sizing and placement, optimal operation in grid-connected and islanded modes, as well as the impact of these resources on power quality, power system security, stability, and protection systems. On the other hand, non-technical challenges can be classified into three categories-regulatory issues, social issues, and economic issues. This Special Issue will address all aspects related to the integration and control of distributed renewable energy resources. It aims to understand the existing challenges and explore new solutions and practices for use in overcoming technical challenges. 606 $aHistory of engineering & technology$2bicssc 606 $aTechnology: general issues$2bicssc 610 $acomposite control strategy 610 $aDG 610 $adiesel generator 610 $adifferent PV technologies 610 $adistributed generation 610 $adistribution networks 610 $adistribution system 610 $aenergy independence 610 $aenergy storage system (ESS) 610 $aGaussian process regression 610 $agreen communities 610 $aHOMER 610 $aintraday forecasting 610 $amachine learning 610 $amicrogrids 610 $amin-max optimisation 610 $amodel-based predictive control 610 $aMonte Carlo simulations 610 $an/a 610 $aoff-grid system 610 $apermanent magnet brushless DC machine (PMBLDCM) 610 $aphotovoltaics 610 $aPLO's profit 610 $apower losses 610 $apower quality 610 $apower quality improvement 610 $apower system management 610 $apower system optimization 610 $apower system reliability 610 $aPV curves 610 $aPV hosting capacity 610 $arobustness 610 $asmart grid paradigm 610 $asmart grids 610 $asolar photovoltaic panel 610 $asolar-powered electric vehicle parking lots 610 $asynchronous machine (SM) 610 $aTSA/SCA 610 $auncertainties 610 $awind turbine 610 $awind turbines 610 $aworst-case scenario 615 7$aHistory of engineering & technology 615 7$aTechnology: general issues 700 $aNazaripouya$b Hamidreza$4edt$01328526 702 $aNazaripouya$b Hamidreza$4oth 906 $aBOOK 912 $a9910566463203321 996 $aIntegration and Control of Distributed Renewable Energy Resources$93038647 997 $aUNINA LEADER 02846nam 2200733z- 450 001 9910557339403321 005 20220111 035 $a(CKB)5400000000042484 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76335 035 $a(oapen)doab76335 035 $a(EXLCZ)995400000000042484 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aBiomedical Image Processing and Classification 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (116 p.) 311 08$a3-0365-0346-3 311 08$a3-0365-0347-1 330 $aBiomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture. 606 $aTechnology: general issues$2bicssc 610 $a3D modeling 610 $aanemia 610 $aartificial intelligence 610 $abinary tree model 610 $ablood vessel segmentation 610 $aconjunctiva 610 $aconvolutional neural network 610 $aconvolutional neural networks 610 $adeep learning 610 $aDICOM processing 610 $adigital pathology 610 $afluid volume assessment 610 $afuzzy clustering 610 $aglomerulosclerosis 610 $aglomerulus detection 610 $ahemoglobin 610 $ahuman tissues 610 $ainferior vena cava 610 $akidney biopsy 610 $akidney fibrosis 610 $akidney transplantation 610 $aMR brain segmentation 610 $an/a 610 $anon-invasive medical device 610 $aobject extraction 610 $apattern recognition 610 $apulsatility 610 $asegmentation 610 $asemantic segmentation 610 $asilhouette analysis 610 $atraining size 610 $aU-Net 610 $aultrasound imaging 615 7$aTechnology: general issues 700 $aMesin$b Luca$4edt$0748261 702 $aMesin$b Luca$4oth 906 $aBOOK 912 $a9910557339403321 996 $aBiomedical Image Processing and Classification$93021391 997 $aUNINA