03895nam a22005295i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050000900153072001600162072002300178072001500201245014200216250001800358264007200376300006700448336002600515337002600541338003600567347002400603490006700627505087600694520100901570650003202579650003102611650002902642650001602671650003102687650003202718650003402750650002902784650001602813650001802829700007902847700007702926700008103003710003403084773002603118776003603144776003603180776003603216830006703252856004603319978-981-97-5718-3DE-He21320251030113258.0cr nn 008mamaa241103s2024    si |    s    |||| 0|eng d  a97898197571837 a10.1007/978-981-97-5718-32doi 4aQ342 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema10aIntelligent Methods in Electrical Power Systemsh[electronic resource] /cedited by Chetan B. Khadse, Ishaan R. Kale, Apoorva S. Shastri.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXIII, 171 p. 100 illus., 79 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aEngineering Optimization: Methods and Applications,x2731-40570 aReview on intelligent methods in Electrical power systems -- Investigation of Electric Load Forecasting Methods: A Weka Application (Regression and Optimization) -- Integration of Intelligent Systems for Efficient Smart Grid Management -- An Application of Artificial Bee Colony and Cohort Intelligence in Automatic Generation Control of Thermal Power System -- Distribution System Losses and Its Allocation: Effects of Load Power Factor and Distributed Generations -- IoT based Intelligent Home Automation System using IFTTT with Google Assistant -- A review on Meta-heuristic Optimization Methods for Efficient Power System Operation -- Ice thickness control circuit to automate the milk chilling system -- SCGB Neural Network based Micro-grid AC Side Fault Analysis -- Artificial intelligence based system for detection and classification of faults in Induction motor.  aThis book provides a comprehensive review of the latest developments in optimization based learning algorithms within the field of electrical engineering. It covers various power system applications including efficient power system operation, load forecasting, fault analysis, home automation and efficient smart grid management. Each application is accompanied by case studies and a literature review in self-contained chapters. The book is dedicated to study the effectiveness of intelligent methods in addressing the power system problems and its mitigation using optimization algorithms. It discusses several optimization algorithms such as random forest algorithm, metaheuristic algorithm, scaled conjugate gradient descent algorithm, artificial bee colony algorithm etc. and their usability in intelligent decision makers for the various optimization problems in electrical engineering. This timely book serves as a practical guide and reference sources for students, researchers and professionals. 0aComputational intelligence. 0aElectric power production. 0aArtificial intelligence. 0aAlgorithms. 0aMathematical optimization.14aComputational Intelligence.24aElectrical Power Engineering.24aArtificial Intelligence.24aAlgorithms.24aOptimization.1 aKhadse, Chetan B.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aKale, Ishaan R.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aShastri, Apoorva S.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981975717608iPrinted edition:z978981975719008iPrinted edition:z9789819757206 0aEngineering Optimization: Methods and Applications,x2731-405740uhttps://doi.org/10.1007/978-981-97-5718-3