Intelligent Data-Analytics for Condition Monitoring
Smart Grid Applications
- 1st Edition - March 1, 2021
- Latest edition
- Authors: Hasmat Malik, Nuzhat Fatema, Atif Iqbal
- Language: English
Intelligent Data-Analytics for Condition Monitoring: Smart Grid Applications looks at intelligent and meaningful uses of data required for an optimized, efficient engineeri… Read more
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.
- 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
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
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 representation1.5 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 Introduction2.2 PV Image Data set collection2.2 Values of data2.3 Experimental design, materials, and methods2.3 Proposed Approach2.4 Deep Convolutional Neural Network (ConvNet/CNN)2.5 Results and Discussion2.6 ConclusionReferences Chapter 3: Intelligent Data Analytics for Power Transformer Health Monitoring Using Modified Fuzzy Q Learning (MFQL)3.1 Introduction3.2 Conventional Techniques Used For DGA Interpretation3.3 Dataset Collection3.3.1 Dataset : Credible Literature3.3.2 Practical DGA Dataset3.3.3 Accuracy Analysis of DGA Performance3.4 MFQL Framework3.4.1 Q-Learning (QL)3.4.2 Fuzzy Q-Learning (FQL)3.4.3 Modified FQL3.5 Input Variable Selection using J48 Algorithm3.6 Fault Classification Using MFQL3.6.1 DGA Training & Testing DATA3.6.2 MFQL Based Fault Classification Model Formation3.7 Results and Discussion3.8 ConclusionReferences Chapter 4: Intelligent Data Analytics for Induction Motor Using Gene Expression Programming (GEP)4.1 Introduction4.2 GEP Methodology and Data Sources4.2.1 Database Used for Study4.2.2 Gene Expression Programming (GEP)4.3 External Fault Classifier based on GEP4.3.1 Data Set: Training and Testing4.3.2 The GEP Approach4.3.3 GEP fault classification model4.4 Results and Discussion4.5 ConclusionReferences Chapter 5: Intelligent Data Analytics for Power Quality Disturbance Analysis Using Multi-Class ELM5.1 Introduction5.2 Model Description5.3 Proposed Framework5.4 Feature Extraction5.5 Most Relevant Input Variable Selection5.6 Multi-Class ELM Framework5.7 Results and Discussion5.8 ConclusionReferences Chapter 6: Intelligent Data Analytics for Transmission Line Fault Diagnosis Using EEMD Based Multiclass SVM and PSVM6.1 Introduction6.2 Methodology6.2.1 Proposed Approach6.2.2 Model Formulation6.2.3 Feature Extraction Using EEMD6.2.4 Support Vector Machine (SVM)6.2.5 Proximal Support Vector Machine (PSVM)6.2.6 SVM and PSVM Based Transmission Line Fault Classification Model Formation6.3 Results and Discussions6.3.1 SVM Based Transmission Line Fault Classification6.3.2 PSVM Based Transmission Line Fault Classification6.3.3 Comparative Results Analysis of SVM and PSVM Based Fault Classification Models6.4 ConclusionReferences 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 Introduction7.2 Related Work7.3 Solar Irradiance Forecasting Methods7.3.1 Conventional Methods7.3.2 AI and Machine Learning Based Methods7.4 Dataset Used for Study7.4.1 Dataset7.4.2 Data Pre-processing7.4.3 Data Analysis7.5 The Structure of Proposed Model7.5.1 Deep Learning Neural Network7.5.2 Performance Evaluation Measures7.6 Results and Discussion7.7 Model Validation7.8 Conclusion References Chapter 8: Intelligent Data Analytics for Wind Speed Forecasting for Wind Power Production Using Long Short-Term memory (LSTM) Network8.1 Introduction8.1.1 Review of Related Works and Motivation8.1.2 Objective and Key Contributions8.2 Proposed Framework Formation8.2.1 Proposed Approach Formation8.2.2 Dataset Collection8.2.3 Dataset Pre-processing8.2.4 Feature Extraction8.2.5 Feature Selection8.2.6 Design of LSTM Network 8.2.7 Performance Measure Indices 8.3 Case Study: Experiments and Discussion8.3.1 The Description of Experimental Dataset8.3.2 Results and Comparisons8.3.3 Comparative Experiments8.4 Conclusion and Future ScopeReferences Chapter 9: Intelligent Data Analytics for Time-Series Load Forecasting Using Fuzzy Reinforcement Learning (FRL)9.1 Introduction9.2 Methodology9.2.1 Proposed Approach9.2.2 Brief Detail of FRL Approach9.2.3 Data Collection 9.3 Time-Series Load Forecasting Model9.3.1 Data Pre-processing Using Different ML Approaches9.3.2 Conventional Model9.3.3 AI and ML Based Model9.3.4 Hybrid Model9.4 Case Studies: Performance Evaluation9.4.1 Minute-Ahead Forecasting9.4.2 Hour-Ahead Forecasting9.4.3 Day-Ahead Forecasting9.4.4 Month-Ahead Forecasting9.5 Conclusion and Future WorkReferences Chapter 10: Intelligent Data Analytics for Battery Charging/Discharging Forecasting Using Semi-supervised and Unsupervised Extreme Learning Machines10.1 Introduction10.2 Methodology10.2.1 Formation of Proposed Approach10.2.2 Health Indication (HI) Extraction10.2.3 Box-Cox Transformation (BCT)10.3.1 BC Transformation10.3.2 BCT Parameter Evaluation Using ML Method10.2.4 Correlation Analysis Using PCA and SRCA Methods Pearson Correlation Analysis (PCA) Spearman Rank Correlation Analysis (SRCA)10.2.5 RUL Estimation Approach10.3 Verification of LIB HI Evaluation10.3.1 LIB Dataset Used for Study10.3.1.1 Charging Condition10.3.1.2 Discharging Condition10.3.1.3 Impedance Measurement Condition10.3.2 Correlation Analysis and Evaluation10.3.2.1 Qualitative Analysis10.3.2.2 Quantitative Analysis10.3.3 HI Performance Evaluation10.4 RUL Estimation Validation10.5 ConclusionReferences
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 representation1.5 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 Introduction2.2 PV Image Data set collection2.2 Values of data2.3 Experimental design, materials, and methods2.3 Proposed Approach2.4 Deep Convolutional Neural Network (ConvNet/CNN)2.5 Results and Discussion2.6 ConclusionReferences Chapter 3: Intelligent Data Analytics for Power Transformer Health Monitoring Using Modified Fuzzy Q Learning (MFQL)3.1 Introduction3.2 Conventional Techniques Used For DGA Interpretation3.3 Dataset Collection3.3.1 Dataset : Credible Literature3.3.2 Practical DGA Dataset3.3.3 Accuracy Analysis of DGA Performance3.4 MFQL Framework3.4.1 Q-Learning (QL)3.4.2 Fuzzy Q-Learning (FQL)3.4.3 Modified FQL3.5 Input Variable Selection using J48 Algorithm3.6 Fault Classification Using MFQL3.6.1 DGA Training & Testing DATA3.6.2 MFQL Based Fault Classification Model Formation3.7 Results and Discussion3.8 ConclusionReferences Chapter 4: Intelligent Data Analytics for Induction Motor Using Gene Expression Programming (GEP)4.1 Introduction4.2 GEP Methodology and Data Sources4.2.1 Database Used for Study4.2.2 Gene Expression Programming (GEP)4.3 External Fault Classifier based on GEP4.3.1 Data Set: Training and Testing4.3.2 The GEP Approach4.3.3 GEP fault classification model4.4 Results and Discussion4.5 ConclusionReferences Chapter 5: Intelligent Data Analytics for Power Quality Disturbance Analysis Using Multi-Class ELM5.1 Introduction5.2 Model Description5.3 Proposed Framework5.4 Feature Extraction5.5 Most Relevant Input Variable Selection5.6 Multi-Class ELM Framework5.7 Results and Discussion5.8 ConclusionReferences Chapter 6: Intelligent Data Analytics for Transmission Line Fault Diagnosis Using EEMD Based Multiclass SVM and PSVM6.1 Introduction6.2 Methodology6.2.1 Proposed Approach6.2.2 Model Formulation6.2.3 Feature Extraction Using EEMD6.2.4 Support Vector Machine (SVM)6.2.5 Proximal Support Vector Machine (PSVM)6.2.6 SVM and PSVM Based Transmission Line Fault Classification Model Formation6.3 Results and Discussions6.3.1 SVM Based Transmission Line Fault Classification6.3.2 PSVM Based Transmission Line Fault Classification6.3.3 Comparative Results Analysis of SVM and PSVM Based Fault Classification Models6.4 ConclusionReferences 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 Introduction7.2 Related Work7.3 Solar Irradiance Forecasting Methods7.3.1 Conventional Methods7.3.2 AI and Machine Learning Based Methods7.4 Dataset Used for Study7.4.1 Dataset7.4.2 Data Pre-processing7.4.3 Data Analysis7.5 The Structure of Proposed Model7.5.1 Deep Learning Neural Network7.5.2 Performance Evaluation Measures7.6 Results and Discussion7.7 Model Validation7.8 Conclusion References Chapter 8: Intelligent Data Analytics for Wind Speed Forecasting for Wind Power Production Using Long Short-Term memory (LSTM) Network8.1 Introduction8.1.1 Review of Related Works and Motivation8.1.2 Objective and Key Contributions8.2 Proposed Framework Formation8.2.1 Proposed Approach Formation8.2.2 Dataset Collection8.2.3 Dataset Pre-processing8.2.4 Feature Extraction8.2.5 Feature Selection8.2.6 Design of LSTM Network 8.2.7 Performance Measure Indices 8.3 Case Study: Experiments and Discussion8.3.1 The Description of Experimental Dataset8.3.2 Results and Comparisons8.3.3 Comparative Experiments8.4 Conclusion and Future ScopeReferences Chapter 9: Intelligent Data Analytics for Time-Series Load Forecasting Using Fuzzy Reinforcement Learning (FRL)9.1 Introduction9.2 Methodology9.2.1 Proposed Approach9.2.2 Brief Detail of FRL Approach9.2.3 Data Collection 9.3 Time-Series Load Forecasting Model9.3.1 Data Pre-processing Using Different ML Approaches9.3.2 Conventional Model9.3.3 AI and ML Based Model9.3.4 Hybrid Model9.4 Case Studies: Performance Evaluation9.4.1 Minute-Ahead Forecasting9.4.2 Hour-Ahead Forecasting9.4.3 Day-Ahead Forecasting9.4.4 Month-Ahead Forecasting9.5 Conclusion and Future WorkReferences Chapter 10: Intelligent Data Analytics for Battery Charging/Discharging Forecasting Using Semi-supervised and Unsupervised Extreme Learning Machines10.1 Introduction10.2 Methodology10.2.1 Formation of Proposed Approach10.2.2 Health Indication (HI) Extraction10.2.3 Box-Cox Transformation (BCT)10.3.1 BC Transformation10.3.2 BCT Parameter Evaluation Using ML Method10.2.4 Correlation Analysis Using PCA and SRCA Methods Pearson Correlation Analysis (PCA) Spearman Rank Correlation Analysis (SRCA)10.2.5 RUL Estimation Approach10.3 Verification of LIB HI Evaluation10.3.1 LIB Dataset Used for Study10.3.1.1 Charging Condition10.3.1.2 Discharging Condition10.3.1.3 Impedance Measurement Condition10.3.2 Correlation Analysis and Evaluation10.3.2.1 Qualitative Analysis10.3.2.2 Quantitative Analysis10.3.3 HI Performance Evaluation10.4 RUL Estimation Validation10.5 ConclusionReferences
- Edition: 1
- Latest edition
- Published: March 1, 2021
- Language: English
HM
Hasmat Malik
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).
Affiliations and expertise
Postdoctoral Scholar, BEARS, Singapore; Assistant Professor, Division of Instrumentation and Control Engineering, Netaji Subhas University of Technology, Delhi, IndiaNF
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), IndiaAI
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
Professor, Department of Electrical Engineering, Qatar University, Doha, QatarRead Intelligent Data-Analytics for Condition Monitoring on ScienceDirect