Monitoring and Control of Electrical Power Systems using Machine Learning Techniques
eBook - ePub

Monitoring and Control of Electrical Power Systems using Machine Learning Techniques

  1. 352 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Monitoring and Control of Electrical Power Systems using Machine Learning Techniques

About this book

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. - Covers advanced applications and solutions for monitoring and control of electrical power systems using machine learning techniques for transmission and distribution systems - Provides deep insight into power quality disturbance detection and classification through machine learning, deep learning, and spatio-temporal algorithms - Includes substantial online supplementary components focusing on dataset generation for machine learning training processes and open-source microgrid model simulators on GitHub

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Yes, you can access Monitoring and Control of Electrical Power Systems using Machine Learning Techniques by Emilio Barocio Espejo,Felix Rafael Segundo Sevilla,Petr Korba in PDF and/or ePUB format, as well as other popular books in Tecnologia e ingegneria & Ingegneria elettronica e telecomunicazioni. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Contributors
  6. Preface
  7. 1: Derivation of generic equivalent models for distribution network analysis using artificial intelligence techniques
  8. 2: Disturbance dataset development for machine-learning-based power quality monitoring in distributed generation systems: a practical guide
  9. 3: Advances in compression algorithms for PMU and Smart Meter data based on tensor decomposition
  10. 4: Machine learning and digital twins: monitoring and control for dynamic security in power systems
  11. 5: Synchrophasor applications in distribution systems: real-life experience
  12. 6: A graph mapping based supervised machine learning strategy for PMU voltage anomalies' detection and classification in distribution networks
  13. 7: Identification of source harmonics in electrical networks using spatiotemporal approaches
  14. 8: Power quality harmonic monitoring by the O-splines-based multiresolution signal decomposition
  15. 9: Monitoring system for identifying power quality issues in distribution networks using Petri nets and Prony method
  16. 10: Dynamic voltage restorer controlled per independent phases for power quality sags-swells mitigation under unbalanced conditions
  17. 11: AI application for load forecasting: a comparison of classical and deep learning methodologies
  18. 12: Study of harmonics in linear, nonlinear nonsinusoidal electrical circuits by geometric algebra
  19. 13: Harmonic sources estimation in distribution systems
  20. Index