Cargando…
Monitoring and control of electrical power systems using machine learning techniques /
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands ; Oxford, United Kingdom ; Cambridge MA :
Elsevier,
[2023]
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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