Intelligent Data-Analytics for Condition Monitoring

Smart Grid Applications
Erscheinungsjahr 2021
ISBN: 978-0-323-85510-5
Verlag: William Andrew Publishing

Buch, Englisch, 268 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 520 g

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.

Zielgruppe

<p>Primary: Researchers working in the field of Integration of Renewable Energy Sources with utility grids, Microgrids, their architecture and control </p> <p>Secondary: Energy engineers, R&D experts and industry professionals working in the field of Renewable and Sustainable Energy. Researcher associates, postgraduate and undergraduate students of the engineering colleges with energy or non-conventional energy resources</p>

Autoren/Hrsg.

Malik, Hasmat

Fatema, Nuzhat

Iqbal, Atif

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze
• Technische Wissenschaften Energietechnik | Elektrotechnik Alternative und erneuerbare Energien

Inhaltsverzeichnis

1. Advances in Machine Learning and Data Analytics

PART A: Intelligent Data Analytics for Classification in Smart Grid2. Intelligent Data Analytics for PV Fault diagnosis Using Deep Convolutional Neural Network (ConvNet/CNN)3. Intelligent Data Analytics for Power Transformer Health Monitoring Using Modified Fuzzy Q Learning (MFQL)4. Intelligent Data Analytics for Induction Motor Using Gene Expression Programming (GEP)5. Intelligent Data Analytics for Power Quality Disturbance Analysis Using Multi-Class ELM6. Intelligent Data Analytics for Transmission Line Fault Diagnosis Using EEMD Based Multiclass SVM and PSVM

PART B: Intelligent Data Analytics for Forecasting in Smart Grid7. Intelligent Data Analytics for Global Solar Radiation Forecasting for Solar Power Production Using Deep Learning Neural Network (DLNN)8. Intelligent Data Analytics for Wind Speed Forecasting for Wind Power Production Using Long Short-Term memory (LSTM) Network9. Intelligent Data Analytics for Time-Series Load Forecasting Using Fuzzy Reinforcement Learning (FRL)10. Intelligent Data Analytics for Battery Charging/Discharging Forecasting Using Semi-supervised and Unsupervised Extreme Learning Machines

Über Autor(innen)

Iqbal, Atif
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.

Fatema, Nuzhat
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.

Malik, Hasmat
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).