Intelligent Data Analytics for Power Apparatus Health Monitoring
AI and Machine Learning Paradigms
Will be published approx. on 1. February 2029
Book
Paperback/Softback
276 pages
978-0-323-91779-7 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
College/higher education
Graduate students, 1st year PhDs and similar early career researchers interested in the uses of AI, machine learning and intelligent data analytics to improve power transformers; Practicing power system engineers. Practitioners working with digital transformers. Expert users of AI, machine learning and data analytics tools working with power conversion analytics.
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 229 mm
Width: 151 mm
ISBN-13
978-0-323-91779-7 (9780323917797)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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.
Editor
Postdoctoral Scholar, BEARS, Singapore; Assistant Professor, Division of Instrumentation and Control Engineering, Netaji Subhas University of Technology, Delhi, India
Singapore Polyclinic, Singapore; Research Associate, National Board of Examinations (NBE), India
Electrical Engineering Department, National Institute of Technology Hamirpur (H.P.), India
Professor, Department of Electrical Engineering, Qatar University, Doha, Qatar
Content
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 and Machine learning (AIML) Based Detailed Review on Introduction to Insulating-Oil of Power Transformer
2. Application of AIML to DGA Based CM-FDD
3. Application of AIML to Dielectric Strength Based CM-FDD
4. Application of AIML to Metal Particle Count Based CM-FDD
5. Application of AIML to Moisture Analysis Based CM-FDD
6. Application of AIML to Power-Factor/Dissipation Factor Based CM-FDD
7. Application of AIML to Interfacial Tension Based CM-FDD
8. Application of AIML to Acid Number Based CM-FDD
9. Application of AIML to Furans Based CM-FDD
10. Application of AIML to Oxygen Inhibitor Based CM-FDD
PART B: Data Analytics for Condition Monitoring and FDD of Power Transformer Windings
11. AIML Based Detailed Review on Introduction to Windings of Power Transformer
12. Application of AIML to SFRA Based CM-FDD
13. Application of AIML to Doble-Tests Based CM-FDD
14. 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 Transformer
16. Application of AIML to Doble-Test Based CM-FDD of BAA
17. Application of AIML to Dielectric Loss Based CM-FDD of BAA
18. Application of AIML to Power Factor Based CM-FDD of BAA
19. Application of AIML to Infrared Camera Based CM-FDD of BAA
20. Application of AIML to Oil-level CM-FDD of Bushing
PART D: Data Analytics for Condition Monitoring and FDD of Power Transformer Core
21. AIML Based Detailed Review on Introduction to Core of Power Transformer
22. Application of AIML to Insulation Resistance Based CM-FDD
23. Application of AIML to Ground Test Based CM-FDD
PART E: Data Analytics for Condition Monitoring and FDD of Power Transformer Conservator
24. AIML Based Detailed Review on Introduction to Conservator of Power Transformer
25. Application of AIML to Oil-leaks /leaks in Diaphragm Based CM-FDD
26. 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 Auxiliaries
27. AIML Based Detailed Review on Introduction to Tanks and Auxiliaries of Power Transformer
28. Application of AIML to Fault Pressure Relay Based CM-FDD
29. Application of AIML to Pressure Relief Devices Based CM-FDD
30. Application of AIML to Buchholz Relay Based CM-FDD
31. Application of AIML to Top-oil/winding/infrared Temperature Indicators Based CM-FDD
32. Application of AIML to Fault/Sound/Vibration Analyzers Based CM-FDD
PART G: Data Analytics for Condition Monitoring and FDD of Power Transformer Cooling System
33. AIML Based Detailed Review on Introduction to Cooling System of Power Transformer
34. Application of AIML to Cleaning Procedure of fan/blades/radiators
35. Application of AIML to Fans and Controls CM-FDD
36. Application of AIML to Oil Pump CM-FDD
37. Application of AIML to Pump Bearings CM-FDD
38. Application of AIML to Radiator CM-FDD
PART A: Data Analytics for Condition Monitoring and FDD of Power Transformer Using Insulating Oil 1. Artificial intelligent and Machine learning (AIML) Based Detailed Review on Introduction to Insulating-Oil of Power Transformer
2. Application of AIML to DGA Based CM-FDD
3. Application of AIML to Dielectric Strength Based CM-FDD
4. Application of AIML to Metal Particle Count Based CM-FDD
5. Application of AIML to Moisture Analysis Based CM-FDD
6. Application of AIML to Power-Factor/Dissipation Factor Based CM-FDD
7. Application of AIML to Interfacial Tension Based CM-FDD
8. Application of AIML to Acid Number Based CM-FDD
9. Application of AIML to Furans Based CM-FDD
10. Application of AIML to Oxygen Inhibitor Based CM-FDD
PART B: Data Analytics for Condition Monitoring and FDD of Power Transformer Windings
11. AIML Based Detailed Review on Introduction to Windings of Power Transformer
12. Application of AIML to SFRA Based CM-FDD
13. Application of AIML to Doble-Tests Based CM-FDD
14. 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 Transformer
16. Application of AIML to Doble-Test Based CM-FDD of BAA
17. Application of AIML to Dielectric Loss Based CM-FDD of BAA
18. Application of AIML to Power Factor Based CM-FDD of BAA
19. Application of AIML to Infrared Camera Based CM-FDD of BAA
20. Application of AIML to Oil-level CM-FDD of Bushing
PART D: Data Analytics for Condition Monitoring and FDD of Power Transformer Core
21. AIML Based Detailed Review on Introduction to Core of Power Transformer
22. Application of AIML to Insulation Resistance Based CM-FDD
23. Application of AIML to Ground Test Based CM-FDD
PART E: Data Analytics for Condition Monitoring and FDD of Power Transformer Conservator
24. AIML Based Detailed Review on Introduction to Conservator of Power Transformer
25. Application of AIML to Oil-leaks /leaks in Diaphragm Based CM-FDD
26. 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 Auxiliaries
27. AIML Based Detailed Review on Introduction to Tanks and Auxiliaries of Power Transformer
28. Application of AIML to Fault Pressure Relay Based CM-FDD
29. Application of AIML to Pressure Relief Devices Based CM-FDD
30. Application of AIML to Buchholz Relay Based CM-FDD
31. Application of AIML to Top-oil/winding/infrared Temperature Indicators Based CM-FDD
32. Application of AIML to Fault/Sound/Vibration Analyzers Based CM-FDD
PART G: Data Analytics for Condition Monitoring and FDD of Power Transformer Cooling System
33. AIML Based Detailed Review on Introduction to Cooling System of Power Transformer
34. Application of AIML to Cleaning Procedure of fan/blades/radiators
35. Application of AIML to Fans and Controls CM-FDD
36. Application of AIML to Oil Pump CM-FDD
37. Application of AIML to Pump Bearings CM-FDD
38. Application of AIML to Radiator CM-FDD