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Intelligent Data-Analytics for Condition Monitoring - 1st Edition - ISBN: 9780323855105, 9780323855112

Intelligent Data-Analytics for Condition Monitoring

1st Edition

Smart Grid Applications

Authors: Hasmat Malik Nuzhat Fatema Atif Iqbal
Paperback ISBN: 9780323855105
eBook ISBN: 9780323855112
Imprint: Academic Press
Published Date: 24th February 2021
Page Count: 268
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Description

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.

Key Features

  • 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

Readership

Researchers working in the field of Integration of Renewable Energy Sources with utility grids, Microgrids, their architecture and control. 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

Table of Contents

Chapter 1: Advances in Machine Learning and Data Analytics
1.1 Introduction
1.2 Data and it relation
1.3 Data pre-processing
1.4 Data visualization and correlation representation
1.5 Application area
1.5.1 Clustering
1.5.2 Regression
1.5.3 Classification
1.5.4 forecasting
1.6 Softwares and techniques used for data analytics
1.7 Sources of datasets for data analytics
1.8 Conclusion
References

PART-A: Intelligent Data Analytics for Classification in Smart Grid

Chapter 2: Intelligent Data Analytics for PV Fault diagnosis Using Deep Convolutional Neural Network (ConvNet/CNN)
2.1 Introduction
2.2 PV Image Data set collection
2.2 Values of data
2.3 Experimental design, materials, and methods
2.3 Proposed Approach
2.4 Deep Convolutional Neural Network (ConvNet/CNN)
2.5 Results and Discussion
2.6 Conclusion
References

Chapter 3: Intelligent Data Analytics for Power Transformer Health Monitoring Using Modified Fuzzy Q Learning (MFQL)
3.1 Introduction
3.2 Conventional Techniques Used For DGA Interpretation
3.3 Dataset Collection
3.3.1 Dataset : Credible Literature
3.3.2 Practical DGA Dataset
3.3.3 Accuracy Analysis of DGA Performance
3.4 MFQL Framework
3.4.1 Q-Learning (QL)
3.4.2 Fuzzy Q-Learning (FQL)
3.4.3 Modified FQL
3.5 Input Variable Selection using J48 Algorithm
3.6 Fault Classification Using MFQL
3.6.1 DGA Training & Testing DATA
3.6.2 MFQL Based Fault Classification Model Formation
3.7 Results and Discussion
3.8 Conclusion
References

Chapter 4: Intelligent Data Analytics for Induction Motor Using Gene Expression Programming (GEP)
4.1 Introduction
4.2 GEP Methodology and Data Sources
4.2.1 Database Used for Study
4.2.2 Gene Expression Programming (GEP)
4.3 External Fault Classifier based on GEP
4.3.1 Data Set: Training and Testing
4.3.2 The GEP Approach
4.3.3 GEP fault classification model
4.4 Results and Discussion
4.5 Conclusion
References

Chapter 5: Intelligent Data Analytics for Power Quality Disturbance Analysis Using Multi-Class ELM
5.1 Introduction
5.2 Model Description
5.3 Proposed Framework
5.4 Feature Extraction
5.5 Most Relevant Input Variable Selection
5.6 Multi-Class ELM Framework
5.7 Results and Discussion
5.8 Conclusion
References

Chapter 6: Intelligent Data Analytics for Transmission Line Fault Diagnosis Using EEMD Based Multiclass SVM and PSVM
6.1 Introduction
6.2 Methodology
6.2.1 Proposed Approach
6.2.2 Model Formulation
6.2.3 Feature Extraction Using EEMD
6.2.4 Support Vector Machine (SVM)
6.2.5 Proximal Support Vector Machine (PSVM)
6.2.6 SVM and PSVM Based Transmission Line Fault Classification Model Formation
6.3 Results and Discussions
6.3.1 SVM Based Transmission Line Fault Classification
6.3.2 PSVM Based Transmission Line Fault Classification
6.3.3 Comparative Results Analysis of SVM and PSVM Based Fault Classification Models
6.4 Conclusion
References

PART-B: Intelligent Data Analytics for Forecasting in Smart Grid

Chapter 7: Intelligent Data Analytics for Global Solar Radiation Forecasting for Solar Power Production Using Deep Learning Neural Network (DLNN)
7.1 Introduction
7.2 Related Work
7.3 Solar Irradiance Forecasting Methods
7.3.1 Conventional Methods
7.3.2 AI and Machine Learning Based Methods
7.4 Dataset Used for Study
7.4.1 Dataset
7.4.2 Data Pre-processing
7.4.3 Data Analysis
7.5 The Structure of Proposed Model
7.5.1 Deep Learning Neural Network
7.5.2 Performance Evaluation Measures
7.6 Results and Discussion
7.7 Model Validation
7.8 Conclusion
References

Chapter 8: Intelligent Data Analytics for Wind Speed Forecasting for Wind Power Production Using Long Short-Term memory (LSTM) Network
8.1 Introduction
8.1.1 Review of Related Works and Motivation
8.1.2 Objective and Key Contributions
8.2 Proposed Framework Formation
8.2.1 Proposed Approach Formation
8.2.2 Dataset Collection
8.2.3 Dataset Pre-processing
8.2.4 Feature Extraction
8.2.5 Feature Selection
8.2.6 Design of LSTM Network
8.2.7 Performance Measure Indices
8.3 Case Study: Experiments and Discussion
8.3.1 The Description of Experimental Dataset
8.3.2 Results and Comparisons
8.3.3 Comparative Experiments
8.4 Conclusion and Future Scope
References

Chapter 9: Intelligent Data Analytics for Time-Series Load Forecasting Using Fuzzy Reinforcement Learning (FRL)
9.1 Introduction
9.2 Methodology
9.2.1 Proposed Approach
9.2.2 Brief Detail of FRL Approach
9.2.3 Data Collection
9.3 Time-Series Load Forecasting Model
9.3.1 Data Pre-processing Using Different ML Approaches
9.3.2 Conventional Model
9.3.3 AI and ML Based Model
9.3.4 Hybrid Model
9.4 Case Studies: Performance Evaluation
9.4.1 Minute-Ahead Forecasting
9.4.2 Hour-Ahead Forecasting
9.4.3 Day-Ahead Forecasting
9.4.4 Month-Ahead Forecasting
9.5 Conclusion and Future Work
References

Chapter 10: Intelligent Data Analytics for Battery Charging/Discharging Forecasting Using Semi-supervised and Unsupervised Extreme Learning Machines
10.1 Introduction
10.2 Methodology
10.2.1 Formation of Proposed Approach
10.2.2 Health Indication (HI) Extraction
10.2.3 Box-Cox Transformation (BCT)
10.3.1 BC Transformation
10.3.2 BCT Parameter Evaluation Using ML Method
10.2.4 Correlation Analysis Using PCA and SRCA Methods
Pearson Correlation Analysis (PCA)
Spearman Rank Correlation Analysis (SRCA)
10.2.5 RUL Estimation Approach
10.3 Verification of LIB HI Evaluation
10.3.1 LIB Dataset Used for Study
10.3.1.1 Charging Condition
10.3.1.2 Discharging Condition
10.3.1.3 Impedance Measurement Condition
10.3.2 Correlation Analysis and Evaluation
10.3.2.1 Qualitative Analysis
10.3.2.2 Quantitative Analysis
10.3.3 HI Performance Evaluation
10.4 RUL Estimation Validation
10.5 Conclusion
References

Chapter 1: Advances in Machine Learning and Data Analytics 
1.1 Introduction 
1.2 Data and it relation 
1.3 Data pre-processing 
1.4 Data visualization and correlation representation
1.5 Application area 
1.5.1 Clustering 
1.5.2 Regression 
1.5.3 Classification 
1.5.4 forecasting 
1.6 Softwares and techniques used for data analytics 
1.7 Sources of datasets for data analytics 
1.8 Conclusion 
References 
   
PART-A: Intelligent Data Analytics for Classification in Smart Grid
   
Chapter 2: Intelligent Data Analytics for PV Fault diagnosis Using Deep Convolutional Neural Network (ConvNet/CNN)
2.1 Introduction
2.2 PV Image Data set collection
2.2 Values of data
2.3 Experimental design, materials, and methods
2.3 Proposed Approach
2.4 Deep Convolutional Neural Network (ConvNet/CNN)
2.5 Results and Discussion
2.6 Conclusion
References
   
Chapter 3: Intelligent Data Analytics for Power Transformer Health Monitoring Using Modified Fuzzy Q Learning (MFQL)
3.1 Introduction
3.2 Conventional Techniques Used For DGA Interpretation
3.3 Dataset  Collection
3.3.1 Dataset : Credible Literature
3.3.2 Practical DGA Dataset
3.3.3 Accuracy Analysis of DGA Performance
3.4 MFQL Framework
3.4.1 Q-Learning (QL)
3.4.2 Fuzzy Q-Learning (FQL)
3.4.3 Modified FQL
3.5 Input Variable Selection using J48 Algorithm
3.6 Fault Classification Using MFQL
3.6.1 DGA Training & Testing DATA
3.6.2 MFQL Based Fault Classification Model Formation
3.7 Results and Discussion
3.8 Conclusion
References
   
Chapter 4: Intelligent Data Analytics for Induction Motor Using Gene Expression Programming (GEP)
4.1 Introduction
4.2 GEP Methodology and Data Sources
4.2.1 Database Used for Study
4.2.2 Gene Expression Programming (GEP)
4.3 External Fault Classifier based on GEP
4.3.1 Data Set: Training and Testing
4.3.2 The GEP Approach
4.3.3 GEP fault classification model
4.4 Results and Discussion
4.5 Conclusion
References
   
Chapter 5: Intelligent Data Analytics for Power Quality Disturbance Analysis Using Multi-Class ELM
5.1 Introduction
5.2 Model Description
5.3 Proposed Framework
5.4 Feature Extraction
5.5 Most Relevant Input Variable Selection
5.6 Multi-Class ELM Framework
5.7 Results and Discussion
5.8 Conclusion
References
   
Chapter 6: Intelligent Data Analytics for Transmission Line Fault Diagnosis Using EEMD Based Multiclass SVM and PSVM
6.1 Introduction
6.2 Methodology
6.2.1 Proposed Approach
6.2.2 Model Formulation
6.2.3 Feature Extraction Using EEMD
6.2.4 Support Vector Machine (SVM)
6.2.5 Proximal Support Vector Machine (PSVM)
6.2.6 SVM and PSVM Based Transmission Line Fault Classification Model Formation
6.3 Results and Discussions
6.3.1 SVM Based Transmission Line Fault Classification
6.3.2 PSVM Based Transmission Line Fault Classification
6.3.3 Comparative Results Analysis of SVM and PSVM Based Fault Classification Models
6.4 Conclusion
References
   
PART-B: Intelligent Data Analytics for Forecasting in Smart Grid
   
Chapter 7: Intelligent Data Analytics for Global Solar Radiation Forecasting for Solar Power Production Using Deep Learning Neural Network (DLNN)
7.1 Introduction
7.2 Related Work
7.3 Solar Irradiance Forecasting Methods
7.3.1 Conventional Methods
7.3.2 AI and Machine Learning Based Methods
7.4 Dataset Used for Study
7.4.1 Dataset
7.4.2 Data Pre-processing
7.4.3 Data Analysis
7.5 The Structure of Proposed Model
7.5.1 Deep Learning Neural Network
7.5.2 Performance Evaluation Measures
7.6 Results and Discussion
7.7 Model Validation
7.8 Conclusion
 References
   
Chapter 8: Intelligent Data Analytics for Wind Speed Forecasting for Wind Power Production Using Long Short-Term memory (LSTM) Network
8.1 Introduction
8.1.1 Review of Related Works and Motivation
8.1.2 Objective and Key Contributions
8.2 Proposed Framework Formation
8.2.1 Proposed Approach Formation
8.2.2 Dataset Collection
8.2.3 Dataset Pre-processing
8.2.4 Feature Extraction
8.2.5 Feature Selection
8.2.6 Design of LSTM Network
8.2.7 Performance Measure Indices
8.3 Case Study: Experiments and Discussion
8.3.1 The Description of Experimental Dataset
8.3.2 Results and Comparisons
8.3.3 Comparative Experiments
8.4 Conclusion and Future Scope
References
   
Chapter 9: Intelligent Data Analytics for Time-Series Load Forecasting Using Fuzzy Reinforcement Learning (FRL)
9.1 Introduction
9.2 Methodology
9.2.1 Proposed Approach
9.2.2 Brief Detail of FRL Approach
9.2.3 Data Collection
9.3 Time-Series Load Forecasting Model
9.3.1 Data Pre-processing Using Different ML Approaches
9.3.2 Conventional Model
9.3.3 AI and ML Based Model
9.3.4 Hybrid Model
9.4 Case Studies: Performance Evaluation
9.4.1 Minute-Ahead Forecasting
9.4.2 Hour-Ahead Forecasting
9.4.3 Day-Ahead Forecasting
9.4.4 Month-Ahead Forecasting
9.5 Conclusion and Future Work
References
   
Chapter 10: Intelligent Data Analytics for Battery Charging/Discharging Forecasting Using Semi-supervised and Unsupervised Extreme Learning Machines
10.1 Introduction
10.2 Methodology
10.2.1 Formation of Proposed Approach
10.2.2 Health Indication (HI) Extraction
10.2.3 Box-Cox Transformation (BCT)
10.3.1 BC Transformation
10.3.2 BCT Parameter Evaluation Using ML Method
10.2.4 Correlation Analysis Using PCA and SRCA Methods
 Pearson Correlation Analysis (PCA)
 Spearman Rank Correlation Analysis (SRCA)
10.2.5 RUL Estimation Approach
10.3 Verification of LIB HI Evaluation
10.3.1 LIB Dataset Used for Study
10.3.1.1 Charging Condition
10.3.1.2 Discharging Condition
10.3.1.3 Impedance Measurement Condition
10.3.2 Correlation Analysis and Evaluation
10.3.2.1 Qualitative Analysis
10.3.2.2 Quantitative Analysis
10.3.3 HI Performance Evaluation
10.4 RUL Estimation Validation
10.5 Conclusion
References

Details

No. of pages:
268
Language:
English
Copyright:
© Academic Press 2021
Published:
24th February 2021
Imprint:
Academic Press
Paperback ISBN:
9780323855105
eBook ISBN:
9780323855112

About the Authors

Hasmat Malik

Dr Hasmat Malik is currently an Assistant Professor in Electrical Engineering, Singapore. He is a Life Member of Indian Society for Technical Education (ISTE), Institution of Electronics and Telecommunication Engineering (IETE), International Association of Engineers, Hong Kong (IAENG), International Society for Research and Development, London (ISRD) and Member of the Institute of Electrical and Electronics Engineers (IEEE), and Mir Labs, Asia. He has published widely in power engineering and power electronics with a particular focus on power transformers and AI / ML techniques.

Affiliations and Expertise

Postdoctoral Scholar, BEARS, Singapore; Assistant Professor, Division of Instrumentation and Control Engineering, Netaji Subhas University of Technology, Delhi, India

Nuzhat Fatema

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.

Affiliations and Expertise

Singapore Polyclinic, Singapore; Research Associate, National Board of Examinations (NBE), India

Atif Iqbal

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.

Affiliations and Expertise

Full Professor, Department of Electrical Engineering, Qatar University; Former Professor, Electrical Engineering, Aligarh Muslim University (AMU)

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