Intelligent Data Analytics for Power Apparatus Health Monitoring

AI and Machine Learning Paradigms

Hasmat Malik, Nuzhat Fatema, Raj Kumar Jarial, Atif Iqbal (Herausgeber)

Buch | Softcover

276 Seiten

2024
Academic Press Inc (Verlag)
978-0-323-91779-7 (ISBN)

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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.

Dr. Hasmat Malik received his Diploma in Electrical Engineering from Aryabhatt Govt. Polytechnic Delhi, B.Tech. degree in electrical & electronics engineering from the GGSIP University, Delhi, M.Tech degree in electrical engineering from National Institute of Technology (NIT) Hamirpur, Himachal Pradesh, and Ph.D in power systems from the Electrical Engineering Department, Indian Institute of Technology (IIT) Delhi, India. He is currently a Postdoctoral Scholar at BEARS, University Town, NUS Campus, Singapore, and an Assistant Professor (on-Leave) at the Division of Instrumentation and Control Engineering, Netaji Subhas University of Technology Delhi, India. A member of various societies, Dr. Malik has published over 100 research articles, including papers in international journals, conferences, and book chapters. He was a Guest Editor of Special Issues of the Journal of Intelligent & Fuzzy Systems, in 2018 and 2020. Dr. Malik has supervised 23 postgraduate students and is involved in several large R&D projects. His principal research interests are artificial intelligence, machine learning, and big-data analytics for renewable energy, smart building & automation, condition monitoring, and online fault detection & 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.

Introduction to intelligent data analytics for power apparatus health monitoring

PART A: Data Analytics for Condition Monitoring and FDD of Power Transformer Using Insulating Oil 1. Artificial intelligent & Machine learning (AIML) Based Detailed Review on Introduction to Insulating-Oil of Power Transformer2. Application of AIML to DGA Based CM-FDD3. Application of AIML to Dielectric Strength Based CM-FDD4. Application of AIML to Metal Particle Count Based CM-FDD5. Application of AIML to Moisture Analysis Based CM-FDD6. Application of AIML to Power-Factor/Dissipation Factor Based CM-FDD7. Application of AIML to Interfacial Tension Based CM-FDD8. Application of AIML to Acid Number Based CM-FDD9. Application of AIML to Furans Based CM-FDD10. Application of AIML to Oxygen Inhibitor Based CM-FDD

PART B: Data Analytics for Condition Monitoring and FDD of Power Transformer Windings11. AIML Based Detailed Review on Introduction to Windings of Power Transformer12. Application of AIML to SFRA Based CM-FDD13. Application of AIML to Doble-Tests Based CM-FDD14. Application of AIML to DC-Resistance, Turn-Ration Percent-Impedance/ Leakage-Reactance Based CM-FDD

PART C: Data Analytics for Condition Monitoring and FDD of Power Transformer Bushing and Arresters (BAA)15. AIML Based Detailed Review on Introduction to Bushing and Arresters of Power Transformer16. Application of AIML to Doble-Test Based CM-FDD of BAA17. Application of AIML to Dielectric Loss Based CM-FDD of BAA18. Application of AIML to Power Factor Based CM-FDD of BAA19. Application of AIML to Infrared Camera Based CM-FDD of BAA20. Application of AIML to Oil-level CM-FDD of Bushing

PART D: Data Analytics for Condition Monitoring and FDD of Power Transformer Core21. AIML Based Detailed Review on Introduction to Core of Power Transformer22. Application of AIML to Insulation Resistance Based CM-FDD23. Application of AIML to Ground Test Based CM-FDD

PART E: Data Analytics for Condition Monitoring and FDD of Power Transformer Conservator24. AIML Based Detailed Review on Introduction to Conservator of Power Transformer25. Application of AIML to Oil-leaks /leaks in Diaphragm Based CM-FDD26. Application of AIML to Inter Air System/ Level Gauge Based CM-FDD

PART F: Data Analytics for Condition Monitoring and FDD of Power Transformer Tank and Auxiliaries27. AIML Based Detailed Review on Introduction to Tanks and Auxiliaries of Power Transformer28. Application of AIML to Fault Pressure Relay Based CM-FDD29. Application of AIML to Pressure Relief Devices Based CM-FDD30. Application of AIML to Buchholz Relay Based CM-FDD31. Application of AIML to Top-oil/winding/infrared Temperature Indicators Based CM-FDD32. Application of AIML to Fault/Sound/Vibration Analyzers Based CM-FDD

PART G: Data Analytics for Condition Monitoring and FDD of Power Transformer Cooling System33. AIML Based Detailed Review on Introduction to Cooling System of Power Transformer34. Application of AIML to Cleaning Procedure of fan/blades/radiators35. Application of AIML to Fans and Controls CM-FDD36. Application of AIML to Oil Pump CM-FDD37. Application of AIML to Pump Bearings CM-FDD38. Application of AIML to Radiator CM-FDD

Erscheint lt. Verlag	1.11.2024
Verlagsort	Oxford
Sprache	englisch
Maße	151 x 229 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
Themenwelt	Technik ► Elektrotechnik / Energietechnik
ISBN-10	0-323-91779-8 / 0323917798
ISBN-13	978-0-323-91779-7 / 9780323917797
Zustand	Neuware