Intelligent Data Analytics for Power Apparatus Health Monitoring: AI and Machine Learning Paradigms - Softcover

Softcover

ISBN 10: 0323917798 ISBN 13: 9780323917797

Publisher: Academic Press, 2029

Synopsis

Intelligent Data Analytics for Power Apparatus Health Monitoring: AI and Machine Learning Paradigms reviews key implementations of machine learning and data analytics techniques for the optimization of digital power transformers. The work addresses health monitoring fully across the constitutive structure of modern transformers, with coverage of DGA-based intelligent data analytics, transformer winding, bushing and arrestor health monitoring, core, conservator, and tank and cooling systems. Chapters address advanced AI/ML methods including deep convolutional neural network, fuzzy reinforcement learning, modified fuzzy Q learning, gene expression programming, extreme-learning machine, and much more.

Primarily intended for researchers and practitioners, the book speeds and simplifies the diagnosis and resolution of health and condition monitoring queries using advanced techniques, particularly with the goal of improved performance, reduced cost, optimized customer behavior and satisfaction, and ultimately increased profitability.

• Discusses pathways to apply machine learning techniques and advanced artificial intelligence algorithms in transformer diagnostics
• Provides demonstrator codes in R, WEKA and MATLAB, supporting direct implementation of intelligent data analytics for each power transformer component
• Demonstrates realistic methods for the step-by-step implementation of advanced AI/ML health monitoring, including convolutional neural network, deep learning and extreme learning machine approaches

About this title

About the Authors

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.

Dr Raj Kumar Jarial is an Associate Professor in the department of electrical engineering, Qatar University, Doha, Qatar. His principle research interests are power electronics applications in electrical drives, power systems, high voltage engineering, renewable energy, smart buildings and automation, condition monitoring and online fault detection and diagnosis (FDD) of power transformers and health monitoring systems.

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.

From the Back Cover

Intelligent Data Analytics for Power Apparatus Health Monitoring: AI and Machine Learning Paradigms reviews key implementations of machine learning and data analytics techniques for the optimization of digital power transformers. The work addresses health monitoring fully across the constitutive structure of modern transformers, with coverage of DGA-based intelligent data analytics, transformer winding, bushing and arrestor health monitoring, core, conservator, and tank and cooling systems. Chapters address advanced AI / ML methods including deep convolutional neural network, fuzzy reinforcement learning, modified fuzzy Q learning, gene expression programming, extreme-learning machine, semi-supervised & unsupervised ELM, proximal support vector machine, deep learning neural network, long short-term memory, and network and deep learning neural networks. Primarily intended for researchers and practitioners, the book speeds and simplifies the diagnosis and resolution of health and condition monitoring queries using advanced techniques, particularly with the goal of improved performance, reduced cost, optimized customer behavior and satisfaction, and ultimately increased profitability.

Edition details

• Publisher: Academic Press
• Publication date: 2029
• Language: English
• ISBN 10: 0323917798
• ISBN 13: 9780323917797
• Binding: Paperback
• Edition number: 1
• Number of pages: 276
• Editor: Malik Hasmat, Fatema Nuzhat, Jarial Raj Kumar, Iqbal Atif