05628nam 2201321z- 450 991057687750332120231214132824.0(CKB)5720000000008400(oapen)https://directory.doabooks.org/handle/20.500.12854/84546(EXLCZ)99572000000000840020202206d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMachine Learning and Data Mining Applications in Power SystemsBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic 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.Technology: general issuesbicsscHistory of engineering & technologybicsscEnergy industries & utilitiesbicsscvirtual power plant (VPP)power quality (PQ)global indexdistributed energy resources (DER)energy storage systems (ESS)power systemslong-term assessmentbattery energy storage systems (BESS)smart gridsconducted disturbancespower qualitysupraharmonics2-150 kHzPower Line Communications (PLC)intentional emissionnon-intentional emissionmains signallingvirtual power plantdata miningclusteringdistributed energy resourcesenergy storage systemsshort term conditionscluster analysis (CA)nonlinear loadsharmonics, cancellation, and attenuation of harmonicswaveform distortionTHDilow-voltage networksoptimization techniquesdifferent batteriesoff-grid microgridintegrated renewable energy systemcluster analysisK-meansagglomerativeANFISfuzzy logicinduction generatorMPPTneural networkrenewable energyvariable speed WECSwind energy conversion systemwind energyfrequency estimationspectrum interpolationpower network disturbancesCOVID-19time-varying reproduction numbersocial distancingload profiledemographic characteristichousehold energy consumptiondemand-side managementenergy managementtime seriesHidden Markov Modelshort-term forecastsparse signal decompositionsupervised dictionary learningdictionary impulsionsingular value decompositiondiscrete cosine transformdiscrete Haar transformdiscrete wavelet transformtransient stability assessmenthome energy managementbinary-coded genetic algorithmsoptimal power schedulingdemand responseData Injection Attackmachine learningcritical infrastructuresmart gridwater treatment plantpower systemTechnology: general issuesHistory of engineering & technologyEnergy industries & utilitiesLeonowicz Zbigniewedt1296134Jasiński MichałedtLeonowicz ZbigniewothJasiński MichałothBOOK9910576877503321Machine Learning and Data Mining Applications in Power Systems3038164UNINA