05671nam 2201345z- 450 991057687750332120220621(CKB)5720000000008400(oapen)https://directory.doabooks.org/handle/20.500.12854/84546(oapen)doab84546(EXLCZ)99572000000000840020202206d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMachine Learning and Data Mining Applications in Power SystemsBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (314 p.)3-0365-4177-2 3-0365-4178-0 This 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.Energy industries and utilitiesbicsscHistory of engineering and technologybicsscTechnology: general issuesbicssc2-150 kHzagglomerativeANFISbattery energy storage systems (BESS)binary-coded genetic algorithmscluster analysiscluster analysis (CA)clusteringconducted disturbancesCOVID-19critical infrastructureData Injection Attackdata miningdemand responsedemand-side managementdemographic characteristicdictionary impulsiondifferent batteriesdiscrete cosine transformdiscrete Haar transformdiscrete wavelet transformdistributed energy resourcesdistributed energy resources (DER)energy managementenergy storage systemsenergy storage systems (ESS)frequency estimationfuzzy logicglobal indexharmonics, cancellation, and attenuation of harmonicsHidden Markov Modelhome energy managementhousehold energy consumptioninduction generatorintegrated renewable energy systemintentional emissionK-meansload profilelong-term assessmentlow-voltage networksmachine learningmains signallingMPPTn/aneural networknon-intentional emissionnonlinear loadsoff-grid microgridoptimal power schedulingoptimization techniquesPower Line Communications (PLC)power network disturbancespower qualitypower quality (PQ)power systempower systemsrenewable energyshort term conditionsshort-term forecastsingular value decompositionsmart gridsmart gridssocial distancingsparse signal decompositionspectrum interpolationsupervised dictionary learningsupraharmonicsTHDitime seriestime-varying reproduction numbertransient stability assessmentvariable speed WECSvirtual power plantvirtual power plant (VPP)water treatment plantwaveform distortionwind energywind energy conversion systemEnergy industries and utilitiesHistory of engineering and technologyTechnology: general issuesLeonowicz Zbigniewedt1296134Jasiński MichałedtLeonowicz ZbigniewothJasiński MichałothBOOK9910576877503321Machine Learning and Data Mining Applications in Power Systems3038164UNINA