|
|
|
|
| LEADER |
00000cam a2200000 i 4500 |
| 001 |
SCIDIR_on1359046349 |
| 003 |
OCoLC |
| 005 |
20231120010721.0 |
| 006 |
m o d |
| 007 |
cr cnu---unuuu |
| 008 |
230123s2023 ne ob 001 0 eng d |
| 040 |
|
|
|a YDX
|b eng
|e rda
|c YDX
|d OPELS
|d N$T
|d YDX
|d UKAHL
|d UKMGB
|d OCLCF
|d OCLCO
|
| 015 |
|
|
|a GBC2K6152
|2 bnb
|
| 016 |
7 |
|
|a 020808963
|2 Uk
|
| 019 |
|
|
|a 1370242976
|
| 020 |
|
|
|a 9780323984041
|q electronic book
|
| 020 |
|
|
|a 0323984045
|q electronic book
|
| 020 |
|
|
|z 9780323999045
|
| 020 |
|
|
|z 0323999042
|
| 035 |
|
|
|a (OCoLC)1359046349
|z (OCoLC)1370242976
|
| 050 |
|
4 |
|a TK1007
|b .M65 2023
|
| 082 |
0 |
4 |
|a 621.317
|2 23/eng/20230220
|
| 245 |
0 |
0 |
|a Monitoring and control of electrical power systems using machine learning techniques /
|c edited by Emilio Barocio Espejo, Felix Rafael Segundo Sevilla, Petr Korba.
|
| 264 |
|
1 |
|a Amsterdam, Netherlands ;
|a Oxford, United Kingdom ;
|a Cambridge MA :
|b Elsevier,
|c [2023]
|
| 300 |
|
|
|a 1 online resource
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 504 |
|
|
|a Includes bibliographical references and index.
|
| 505 |
0 |
|
|a 1 Derivation of generic equivalent models for distribution network analysis using artificial intelligence techniques -- l2 Disturbance dataset development for machine-learning-based power quality monitoring in distributed generation systems: a practical guide -- l3 Advances in compression algorithms for PMU and Smart Meter data based on tensor decomposition -- l4 Machine learning and digital twins: monitoring and control for dynamic security in power systems -- l5 Synchrophasor applications in distribution systems: real-life experience -- l6 A graph mapping based supervised machine learning strategy for PMU voltage anomalies' detection and classification in distribution networks -- l7 Identification of source harmonics in electrical networks using spatiotemporal approaches -- l8 Power quality harmonic monitoring by the O-splines-based multiresolution signal decomposition -- l9 Monitoring system for identifying power quality issues in distribution networks using Petri nets and Prony method -- l10 Dynamic voltage restorer controlled per independent phases for power quality sags-swells mitigation under unbalanced conditions -- l11 AI application for load forecasting: a comparison of classical and deep learning methodologies -- l12 Study of harmonics in linear, nonlinear nonsinusoidal electrical circuits by geometric algebra -- l13 Harmonic sources estimation in distribution systems
|
| 588 |
|
|
|a Description based on online resource; title from digital title page (viewed on March 16, 2023).
|
| 650 |
|
0 |
|a Electric power systems
|x Control.
|
| 650 |
|
0 |
|a Machine learning.
|
| 650 |
|
6 |
|a R�eseaux �electriques (�Energie)
|x R�egulation.
|0 (CaQQLa)201-0079523
|
| 650 |
|
6 |
|a Apprentissage automatique.
|0 (CaQQLa)201-0131435
|
| 650 |
|
7 |
|a Electric power systems
|x Control
|2 fast
|0 (OCoLC)fst00905538
|
| 650 |
|
7 |
|a Machine learning
|2 fast
|0 (OCoLC)fst01004795
|
| 700 |
1 |
|
|a Espejo, Emilio Barocio,
|e editor.
|
| 700 |
1 |
|
|a Sevilla, Felix Rafael Segundo,
|e editor.
|
| 700 |
1 |
|
|a Korba, Petr,
|e editor.
|
| 776 |
0 |
8 |
|i Print version:
|z 0323999042
|z 9780323999045
|w (OCoLC)1321786639
|
| 776 |
0 |
8 |
|i Print version:
|t Monitoring and control of electrical power systems using machine learning techniques
|z 9780323999045
|w (OCoLC)1351696310
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780323999045
|z Texto completo
|
