04474nam 2200733 450 991020895960332120221206104937.01-118-72934-X1-118-96048-31-322-14990-910.1002/9781118960486(CKB)3710000000412802(SSID)ssj0001333423(PQKBManifestationID)11914129(PQKBTitleCode)TC0001333423(PQKBWorkID)11385855(PQKB)10615944(DLC) 2014023946(Au-PeEL)EBL1782540(CaPaEBR)ebr10930132(CaONFJC)MIL646245(OCoLC)881498630(CaBNVSL)mat08371502(IDAMS)0b00006487da58ec(IEEE)8371502(CaSebORM)9781118960530(MiAaPQ)EBC1782540(PPN)235764191(EXLCZ)99371000000041280220200313d2019 uy engurcnu||||||||txtccrMulti-terminal direct-current grids modeling, analysis, and control /Nilanjan Ray Chaudhuri, Balarko Chaudhuri, Rajat Majumder, Amirnaser Yazdani1st editionHoboken, New Jersey :IEEE / Wiley & Sons, Inc.,2014.[Piscataqay, New Jersey] :IEEE Xplore,[2014]1 online resource (314 pages) illustrationsBibliographic Level Mode of Issuance: Monograph1-118-72910-2 1-118-96053-X Includes bibliographical references at the end of each chapters and index.Cover; Title Page; Copyright; Dedication; Foreword; Preface; Acronyms; Symbols; Chapter 1: Fundamentals; 1.1 Introduction; 1.2 Rationale Behind MTDC Grids; 1.3 Network Architectures of MTDC Grids; 1.4 Enabling Technologies and Components of MTDC Grids; 1.5 Control Modes in MTDC Grid; 1.6 Challenges for MTDC Grids; 1.7 Configurations of MTDC Converter Stations; 1.8 Research Initiatives on MTDC Grids; 1.9 Focus and Scope of the Monograph; Chapter 2: The Voltage-Sourced Converter (VSC); 2.1 Introduction; 2.2 Ideal Voltage-Sourced Converter; 2.3 Practical Voltage-Sourced Converter; 2.4 Control.2.5 Simulation2.6 Symbols of the VSC; Chapter 3: Modeling, Analysis, and Simulation of AC-MTDC Grids; 3.1 Introduction; 3.2 MTDC Grid Model; 3.3 AC Grid Model; 3.4 AC-MTDC Load flow Analysis; 3.5 AC-MTDC Grid Model for Nonlinear Dynamic Simulation; 3.6 Small-signal Stability Analysis of AC-MTDC Grid; 3.7 Transient Stability Analysis of AC-MTDC Grid; 3.8 Case Studies; 3.9 Case Study 1: The North Sea Benchmark System; 3.10 Case Study 2: MTDC Grid Connected to Equivalent AC Systems; 3.11 Case Study 3: MTDC Grid Connected to Multi-machine AC System; Chapter 4: Autonomous Power Sharing.4.1 Introduction4.2 Steady-state Operating Characteristics; 4.3 Concept of Power Sharing; 4.4 Power Sharing in MTDC Grid; 4.5 AC-MTDC Grid Load flow Solution; 4.6 Post-contingency Operation; 4.7 Linear Model; 4.8 Case Study; Chapter 5: Frequency Support; 5.1 Introduction; 5.2 Fundamentals of Frequency Control; 5.3 Inertial and Primary Frequency Support from Wind Farms; 5.4 Wind Farms in Secondary Frequency Control (AGC); 5.5 Modified Droop Control for Frequency Support; 5.6 AC-MTDC Load Flow Solution; 5.7 Post-Contingency Operation; 5.8 Case Study; Chapter 6: Protection of MTDC Grids.6.1 Introduction6.2 Converter Station Protection; 6.3 DC Cable Fault Response; 6.4 Fault-blocking Converters; 6.5 DC Circuit Breakers; 6.6 Protection Strategies; References; Index; End User License Agreement."Presents a comprehensive modeling framework for MTDC grids which is compatible with the standard AC system modeling for stability studies"--Provided by publisher.Electric power distributionDirect currentElectric power distributionDirect current.621.31/2TEC031000bisacshChaudhuri Nilanjan Ray1981-968461Chaudhuri Balarko1977-294859Majumder Rajat1977-995242Yazdani Amirnaser1972-845707CaBNVSLCaBNVSLCaBNVSLBOOK9910208959603321Multi-terminal direct-current grids2279986UNINA03440nam 22007215 450 991100146270332120250506074726.0981-9634-98-910.1007/978-981-96-3498-9(CKB)38753324300041(DE-He213)978-981-96-3498-9(MiAaPQ)EBC32077310(Au-PeEL)EBL32077310(EXLCZ)993875332430004120250503d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAdvances in Deep Learning, Volume 2 /by M. Arif Wani, Bisma Sultan, Sarwat Ali, Mukhtar Ahmad Sofi1st ed. 2025.Singapore :Springer Nature Singapore :Imprint: Springer,2025.1 online resource (XVI, 186 p. 94 illus., 62 illus. in color.) Studies in Big Data,2197-6511 ;12981-9634-97-0 Introduction to Deep Learning Architectures -- Evolutionary Algorithm-based Neural Architecture SearchEvolutionary Algorithm-based Neural Architecture Search -- Gradient-based Neural Architecture Search.This book describes novel ways of using deep learning to solve real-world problems. It covers advanced deep learning topics like neural architecture search, ensemble deep learning, transfer learning techniques, lightweight architectures, hybrid deep learning approaches, and generative adversarial networks. The book discusses the use of these advanced topics in selected applications like image classification, object detection, image steganography, protein secondary structure prediction, and gene expression data classification. Various challenges and future research directions falling under the scope of these topics are discussed.Studies in Big Data,2197-6511 ;12Computational intelligenceBig dataNeural networks (Computer science)Image processingDigital techniquesComputer visionArtificial intelligenceComputational IntelligenceBig DataMathematical Models of Cognitive Processes and Neural NetworksComputer Imaging, Vision, Pattern Recognition and GraphicsArtificial IntelligenceComputational intelligence.Big data.Neural networks (Computer science)Image processingDigital techniques.Computer vision.Artificial intelligence.Computational Intelligence.Big Data.Mathematical Models of Cognitive Processes and Neural Networks.Computer Imaging, Vision, Pattern Recognition and Graphics.Artificial Intelligence.006.3Wani M. Arifauthttp://id.loc.gov/vocabulary/relators/aut1006177Sultan Bismaauthttp://id.loc.gov/vocabulary/relators/autAli Sarwatauthttp://id.loc.gov/vocabulary/relators/autSofi Mukhtar Ahmadauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9911001462703321Advances in Deep Learning, Volume 24385140UNINA