Description
Intelligent Data-Analytics for Condition Monitoring
Smart Grid Applications
Authors: Malik Hasmat, Fatema Nuzhat, Iqbal Atif
Language: EnglishSubject for Intelligent Data-Analytics for Condition Monitoring:
Keywords
actual operating condition; artificial intelligence; artificial neural network; Box-Cox transformation; classification; condition monitoring; ConvNet/CNN; data analytics; data preprocessing; dataset sources; deep neural network; demonstration; DGA; diagnosis; EEMD; ELM; EMD; failure analysis; fault classification; fault detection and diagnosis; fault diagnosis; FDD; feature extraction; feature selection; forecasting; fuzzy reinforcement learning; gene expression programming; health indicator; IMFs; induction motor; J48 algorithm; lithium-ion battery; long short-term memory (LSTM); MFQL; MLP; MLP-ANN; online monitoring; open access; power quality; power system; prediction; PSVM; PV module failure; PV system; remaining-useful-life (RUL); software; solar radiation; SVM; transmission line; vertical power plant; visualization; WECS; wind speed
159.65 €
In Print (Delivery period: 14 days).
Add to cart the book of Malik Hasmat, Fatema Nuzhat, Iqbal Atif268 p. · 15x22.8 cm · Paperback
Description
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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.
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
Primary: Researchers working in the field of Integration of Renewable Energy Sources with utility grids, Microgrids, their architecture and control
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
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, comple
- Features deep learning methodologies in smart grid deployment and maintenance applications
- Includes coding for intelligent data analytics for each application
- Covers advanced problems and solutions of smart grids using advance data analytic techniques