| Description |
1 online resource (xiv, 339 pages) : illustrations |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
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 |
| Summary |
"Monitoring and Control of Electrical Power Systems using Machine Learning Techniques bridges the gap between advanced machine learning techniques and their application in the control and monitoring of electrical power systems, particularly relevant for heavily distributed energy systems and real-time application. The book reviews key applications of deep learning, spatio-temporal, and advanced signal processing methods for monitoring power quality. This reference introduces guiding principles for the monitoring and control of power quality disturbances arising from integration of power electronic devices and discusses monitoring and control of electrical power systems using benchmark test systems for the creation of bespoke advanced data analytic algorithms."--Provided by Publisher. |
| Note |
Description based on online resource; title from digital title page (viewed on March 16, 2023). |
| Subject |
Electric power systems -- Control.
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Machine learning.
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Réseaux électriques (Énergie) -- Régulation.
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Apprentissage automatique.
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Electric power systems -- Control
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Machine learning
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| Added Author |
Espejo, Emilio Barocio, editor.
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Sevilla, Felix Rafael Segundo, editor.
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Korba, Petr, editor.
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| Other Form: |
Print version: 0323999042 9780323999045 (OCoLC)1321786639 |
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Print version: Monitoring and control of electrical power systems using machine learning techniques 9780323999045 (OCoLC)1351696310 |
| ISBN |
9780323984041 electronic book |
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0323984045 electronic book |
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9780323999045 |
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0323999042 |
| Standard No. |
AU@ 000073485905 |
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UKMGB 020808963 |
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AU@ 000074362621 |
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