Intelligent Data-Analytics for Condition Monitoring: Smart Grid Applications
Autor Hasmat Malik, Nuzhat Fatema, Atif Iqbalen Limba Engleză Paperback – 28 feb 2021
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.
- 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
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Specificații
ISBN-13: 9780323855105
ISBN-10: 0323855105
Pagini: 268
Dimensiuni: 152 x 229 mm
Greutate: 0.36 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0323855105
Pagini: 268
Dimensiuni: 152 x 229 mm
Greutate: 0.36 kg
Editura: ELSEVIER SCIENCE
Public țintă
Primary: Researchers working in the field of Integration of Renewable Energy Sources with utility grids, Microgrids, their architecture and controlSecondary: 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
Cuprins
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
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