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Intelligent Data-Analytics for Condition Monitoring: Smart Grid Applications - Brossura

Malik, Hasmat; Fatema, Nuzhat; Iqbal, Atif

 
9780323855105: Intelligent Data-Analytics for Condition Monitoring: Smart Grid Applications
Brossura
ISBN 10: 0323855105 ISBN 13: 9780323855105
Casa editrice: Academic Press, 2021

Sinossi

Intelligent Data-Analytics for Condition Monitoring: Smart Grid Applications looks at intelligent and meaningful uses of data required for an optimized, efficient engineering processes. In addition, the book provides application perspectives of various deep learning models for the condition monitoring of electrical equipment. With chapters discussing the fundamentals of machine learning and data analytics, the book is divided into two parts, including i) The application of intelligent data analytics in Solar PV fault diagnostics, transformer health monitoring and faults diagnostics, and induction motor faults and ii) Forecasting issues using data analytics which looks at global solar radiation forecasting, wind data forecasting, and more.

This reference is useful for all engineers and researchers who need preliminary knowledge on data analytics fundamentals and the working methodologies and architecture of smart grid systems.

  • Features deep learning methodologies in smart grid deployment and maintenance applications
  • Includes coding for intelligent data analytics for each application
  • Covers advanced problems and solutions of smart grids using advance data analytic techniques

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sugli autori

Hasmat Malik is a Senior Lecturer in the Department of Electrical Power Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia. His research interests include artificial intelligence, machine learning, and big-data analytics applied to renewable energy, smart building automation, condition monitoring, and online fault detection and diagnosis (FDD).



Dr Nuzhat Fatema has 10 years of experience in Intelligent data analytics using AI & Machine learning for hospital and health care management. Dr. Fatema is the founder of the Intelligent-Prognostic (iPrognostic) Pvt. Ltd. Her area of interest is AI, ML and intelligent data analytics application in healthcare, monitoring, prediction, forecasting, detection and diagnosis to optimize decision-making in diagnosis, management and industry care.

Atif Iqbal, is a Professor in Electrical Engineering, Qatar University. He publishes widely in power electronics, variable speed drives and renewable energy sources. Dr. Iqbal has co-authored more than 400 research papers and two books. His principal area of research interest is smart grids, complex energy transitions, active distribution networks, electric vehicles drivetrains, sustainable development and energy security, and distributed energy generation.

Dalla quarta di copertina

Intelligent Data-Analytics for Condition Monitoring: Smart Grid Applications looks at intelligent and meaningful uses of data required for an optimized and efficient engineering processes and provides application perspectives of various deep learning models for the condition monitoring of electrical equipment.

With chapters discussing the fundamentals of machine learning and data analytics, the book is in two parts; i) the application of intelligent data analytics in Solar PV fault diagnostics, transformer health monitoring and faults diagnostics, and induction motor faults and ii) forecasting issues using data analytics which looks at global solar radiation forecasting, wind data forecasting and load forecasting demand side management.

This reference is useful for all engineers and researchers needing a preliminary knowledge on data analytics fundamentals and the working methodologies and architecture of smart grid systems.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Academic Press
Data di pubblicazione
2021
Lingua
Inglese
ISBN 10 
0323855105
ISBN 13 
9780323855105
Rilegatura
Copertina flessibile
Numero edizione
1
Numero di pagine
270
Contatto del produttore
Academic Press
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