Academic Press Intelligent Data-Analytics for Condition Monitoring: Smart Grid Applications

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. Review A preliminary resource on data analytics fundamentals and the working methodology and architecture of smart grid systems From the Back Cover 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. About the Author Dr Hasmat Malik is currently an Assistant Professor in Electrical Engineering, Singapore. He is a Life Member of Indian Society for Technical Education (ISTE), Institution of Electronics and Telecommunication Engineering (IETE), International Association of Engineers, Hong Kong (IAENG), International Society for Research and Development, London (ISRD) and Member of the Institute of Electrical and Electronics Engineers (IEEE), and Mir Labs, Asia. He has published widely in power engineering and power electronics with a particular focus on power transformers and AI / ML techniques. 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.