LEADER 00902nam0-2200289 --450 001 9910843200503321 005 20240409121454.0 010 $a978-88-548-1498-1 100 $a20240409d2007----kmuy0itay5050 ba 101 0 $aita 102 $aIT 105 $a 001yy 200 1 $a<>personalità filosofica di Marco Tullio Cicerone$eMonte Compatri 2006$fa cura di Pietro Ciaravolo 210 $aRoma$cAracne$d2007 215 $a219 p.$d21 cm 225 1 $aA11$v274 300 $aAtti del Convegno 300 $aIn testa al front.: Centro per la filosofia italiana. 702 1$aCiaravolo,$bPietro 712 02$aCentro per la filosofia italiana 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910843200503321 952 $aDAM A88 CIAP 01$b2024/4563$fFLFBC 959 $aFLFBC 996 $aPersonalità filosofica di Marco Tullio Cicerone$94149668 997 $aUNINA LEADER 05671nam 2201345z- 450 001 9910576877503321 005 20220621 035 $a(CKB)5720000000008400 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84546 035 $a(oapen)doab84546 035 $a(EXLCZ)995720000000008400 100 $a20202206d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine Learning and Data Mining Applications in Power Systems 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (314 p.) 311 08$a3-0365-4177-2 311 08$a3-0365-4178-0 330 $aThis Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid's reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries. 606 $aEnergy industries and utilities$2bicssc 606 $aHistory of engineering and technology$2bicssc 606 $aTechnology: general issues$2bicssc 610 $a2-150 kHz 610 $aagglomerative 610 $aANFIS 610 $abattery energy storage systems (BESS) 610 $abinary-coded genetic algorithms 610 $acluster analysis 610 $acluster analysis (CA) 610 $aclustering 610 $aconducted disturbances 610 $aCOVID-19 610 $acritical infrastructure 610 $aData Injection Attack 610 $adata mining 610 $ademand response 610 $ademand-side management 610 $ademographic characteristic 610 $adictionary impulsion 610 $adifferent batteries 610 $adiscrete cosine transform 610 $adiscrete Haar transform 610 $adiscrete wavelet transform 610 $adistributed energy resources 610 $adistributed energy resources (DER) 610 $aenergy management 610 $aenergy storage systems 610 $aenergy storage systems (ESS) 610 $afrequency estimation 610 $afuzzy logic 610 $aglobal index 610 $aharmonics, cancellation, and attenuation of harmonics 610 $aHidden Markov Model 610 $ahome energy management 610 $ahousehold energy consumption 610 $ainduction generator 610 $aintegrated renewable energy system 610 $aintentional emission 610 $aK-means 610 $aload profile 610 $along-term assessment 610 $alow-voltage networks 610 $amachine learning 610 $amains signalling 610 $aMPPT 610 $an/a 610 $aneural network 610 $anon-intentional emission 610 $anonlinear loads 610 $aoff-grid microgrid 610 $aoptimal power scheduling 610 $aoptimization techniques 610 $aPower Line Communications (PLC) 610 $apower network disturbances 610 $apower quality 610 $apower quality (PQ) 610 $apower system 610 $apower systems 610 $arenewable energy 610 $ashort term conditions 610 $ashort-term forecast 610 $asingular value decomposition 610 $asmart grid 610 $asmart grids 610 $asocial distancing 610 $asparse signal decomposition 610 $aspectrum interpolation 610 $asupervised dictionary learning 610 $asupraharmonics 610 $aTHDi 610 $atime series 610 $atime-varying reproduction number 610 $atransient stability assessment 610 $avariable speed WECS 610 $avirtual power plant 610 $avirtual power plant (VPP) 610 $awater treatment plant 610 $awaveform distortion 610 $awind energy 610 $awind energy conversion system 615 7$aEnergy industries and utilities 615 7$aHistory of engineering and technology 615 7$aTechnology: general issues 700 $aLeonowicz$b Zbigniew$4edt$01296134 702 $aJasin?ski$b Micha?$4edt 702 $aLeonowicz$b Zbigniew$4oth 702 $aJasin?ski$b Micha?$4oth 906 $aBOOK 912 $a9910576877503321 996 $aMachine Learning and Data Mining Applications in Power Systems$93038164 997 $aUNINA