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. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Editors Biography - p. 8 7. Preface - p. 12 8. Part A: Intelligent Data Analytics for Classification in Smart Grid - p. 17 9. Chapter 1. Advances in Machine Learning and Data Analytics - p. 17 10. Chapter 2. Intelligent Data Analytics for PV Fault Diagnosis Using Deep Convolutional Neural Network (ConvNet/CNN) - p. 30 11. Chapter 3. Intelligent Data Analytics for Power Transformer Health Monitoring Using Modified Fuzzy Q Learning (MFQL) - p. 44 12. Chapter 4. Intelligent Data Analytics for 3-Phase Induction Motor Fault Diagnosis Using Gene Expression Programming (GEP) - p. 66 13. Chapter 5. Intelligent Data Analytics for Power Quality Disturbance Diagnosis Using Extreme Learning Machine (ELM) - p. 90 14. Chapter 6. Intelligent Data Analytics for Transmission Line Fault Diagnosis Using EEMD-Based Multiclass SVM and PSVM - p. 114 15. Part B: Intelligent Data Analytics for Forecasting in Smart Grid - p. 140 16. Chapter 7. Intelligent Data Analytics for Global Solar Radiation Forecasting for Solar Power Production Using Deep Learning Neural Network (DLNN) - p. 142 17. Chapter 8. Intelligent Data Analytics for Wind Speed Forecasting for Wind Power Production Using Long Short-Term Memory (LSTM) Network - p. 164 18. Chapter 9. Intelligent Data Analytics for Time-Series Load Forecasting Using Fuzzy Reinforcement Learning (FRL) - p. 192 19. Chapter 10. Intelligent Data Analytics for Battery Health Forecasting Using Semi-Supervised and Unsupervised Extreme Learning Machines - p. 214 20. Index - p. 242
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. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Editors Biography - p. 8 7. Preface - p. 12 8. Part A: Intelligent Data Analytics for Classification in Smart Grid - p. 17 9. Chapter 1. Advances in Machine Learning and Data Analytics - p. 17 10. Chapter 2. Intelligent Data Analytics for PV Fault Diagnosis Using Deep Convolutional Neural Network (ConvNet/CNN) - p. 30 11. Chapter 3. Intelligent Data Analytics for Power Transformer Health Monitoring Using Modified Fuzzy Q Learning (MFQL) - p. 44 12. Chapter 4. Intelligent Data Analytics for 3-Phase Induction Motor Fault Diagnosis Using Gene Expression Programming (GEP) - p. 66 13. Chapter 5. Intelligent Data Analytics for Power Quality Disturbance Diagnosis Using Extreme Learning Machine (ELM) - p. 90 14. Chapter 6. Intelligent Data Analytics for Transmission Line Fault Diagnosis Using EEMD-Based Multiclass SVM and PSVM - p. 114 15. Part B: Intelligent Data Analytics for Forecasting in Smart Grid - p. 140 16. Chapter 7. Intelligent Data Analytics for Global Solar Radiation Forecasting for Solar Power Production Using Deep Learning Neural Network (DLNN) - p. 142 17. Chapter 8. Intelligent Data Analytics for Wind Speed Forecasting for Wind Power Production Using Long Short-Term Memory (LSTM) Network - p. 164 18. Chapter 9. Intelligent Data Analytics for Time-Series Load Forecasting Using Fuzzy Reinforcement Learning (FRL) - p. 192 19. Chapter 10. Intelligent Data Analytics for Battery Health Forecasting Using Semi-Supervised and Unsupervised Extreme Learning Machines - p. 214 20. Index - p. 242
