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Smart Electrical and Mechanical Systems
An Application of Artificial Intelligence and Machine Learning
- 1st Edition - June 21, 2022
- Editors: Rakesh Sehgal, Neeraj Gupta, Anuradha Tomar, Mukund Dutt Sharma, Vigna Kumaran
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 7 8 9 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 1 4 4 1 - 3
Smart Electrical and Mechanical Systems: An Application of Artificial Intelligence and Machine Learning is an international contributed work with the most up-to-date fundament… Read more
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Request a sales quoteSmart Electrical and Mechanical Systems: An Application of Artificial Intelligence and Machine Learning is an international contributed work with the most up-to-date fundamentals and conventional methods used in smart electrical and mechanical systems. Detailing methods and procedures for the application of ML and AI, it is supported with illustrations of the systems, process diagrams visuals of the systems and/or their components, and supportive data and results leading to the benefits and challenges of the relevant applications. The multidisciplinary theme of the book will help researchers build a synergy between electrical and mechanical engineering systems.
The book guides readers on not only how to effectively solve problems but also provide high accuracy needed for successful implementation. Interdisciplinary in nature, the book caters to the needs of the electrical and mechanical engineering industry by offering details on the application of AI and ML in robotics, design and manufacturing, image processing, power system operation and forecasting with suitable examples.
- Includes significant case studies related to application of Artificial Intelligence and Machine Learning in Energy and Power, Mechanical Design and Manufacturing
- Contains supporting illustrations and tables, along with a valuable set of references at the end of each chapter
- Provides original, state-of-the-art research material written by international and national respected contributors
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Chapter One. Machine learning and its applications
- 1. Artificial intelligence
- 2. Machine learning and deep learning
- 3. Real life examples of deep learning and machine learning
- 4. Components of machine learning
- 5. Application areas of machine learning and deep learning
- 6. Benchmark dataset(s)
- 7. Conclusion
- Chapter Two. A Multiple Object Recognition Approach via DenseNet-161 Model
- 1. Introduction
- 2. Related work
- 3. DenseNet architecture
- 4. Methodology
- 5. Result analysis and discussion
- 6. Conclusion
- Chapter Three. Deep learning-based image processing for analyzing combustion behavior of gel fuel droplets
- 1. Introduction
- 2. Experimental methodology
- 3. Edge detection methods
- 4. Holistically nested edge detection
- 5. Results
- 6. Concluding remarks
- Chapter Four. Deep learning-based methods for detecting surface defects in steel plates
- 1. Introduction
- 2. Dataset
- 3. Metrics
- 4. Methodology
- 5. Results and inference
- 6. Conclusions
- Chapter Five. Extremum center interpolation-based EMD approach for fault detection of reciprocating compressor
- 1. Introduction
- 2. Theoretical background
- 3. Proposed methodology
- 4. Experimental setup, results, and discussion
- 5. Conclusion
- Chapter Six. Renewable energy sources forecasting and integration using machine learning
- 1. Introduction
- 2. Prediction of renewable energy sources
- 3. Determination of plant size, location, and configuration
- 4. Grid management
- 5. Maximum power point tracking
- 6. Conclusion
- Chapter Seven. Role of big data analytic and machine learning in power system contingency analysis
- 1. Introduction
- 2. Contingency analysis
- 3. Voltage stability analysis
- 4. Applications of big data to power system
- 5. Machine learning in power system application
- 6. Proposed algorithm
- 7. Case study and results
- 8. Conclusion
- Chapter Eight. Ensemble classifier-based protection scheme for hybrid microgrid
- 1. Introduction
- 2. Modeling and simulation of hybrid microgrid system
- 3. Selection of critical buses for sensor placement using optimization algorithm
- 4. Development of ESD-based protection scheme for hybrid microgrid
- 5. Performance analysis
- 6. Conclusion
- Chapter Nine. A linear discriminant analysis based protection scheme for DC microgrid under stressed scenarios
- Acronyms
- 1. Introduction
- 2. DC-microgrid architecture and fault analysis
- 3. DC test microgrid system under study
- 4. Linear discriminant analysis-based protection scheme
- 5. Flowchart of the proposed LDA-based scheme
- 6. Performance validation
- 7. Conclusions
- Chapter Ten. Machine learning application to power system forecasting
- 1. Introduction
- 2. Forecasting for probabilistic power system planning
- 3. Data preprocessing steps
- 4. Probabilistic forecasting using machine learning algorithms
- 5. Performance comparison of well-established probabilistic PV generation forecasting models
- 6. Conclusion
- Chapter Eleven. Machine learning application to industrial control systems
- 1. Introduction
- 2. Modeling strategies of continuous stirred tank reactor
- 3. Controlling strategies of continuous stirred tank reactor
- 4. Neural network–based modeling and control of CSTR
- 5. Results and discussions
- 6. Conclusion
- Chapter Twelve. Data-driven based optimal power system operation and control in a weak grid
- 1. Introduction
- 2. Overview of data-driven-based MG
- 3. Potential applications of data-driven analysis in smart power grids (Table 12.1)
- 4. Microgrid structure and its working
- 5. Networked microgrids
- 6. Intelligent energy storage devices
- 7. Operational aspects for distribution system
- 8. Recent available technologies and their outstanding results
- 9. Conclusion and future scope
- Chapter Thirteen. Short-term load forecasting in the presence of grid uncertainties using new methods based on deep learning
- 1. Introduction
- 2. Different aspects of LF
- 3. LF classification and evaluation criteria
- 4. DL methods
- 5. Forecasting results analysis and compare
- 6. Conclusion
- Index
- No. of pages: 314
- Language: English
- Edition: 1
- Published: June 21, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323907897
- eBook ISBN: 9780323914413
RS
Rakesh Sehgal
NG
Neeraj Gupta
AT
Anuradha Tomar
MS
Mukund Dutt Sharma
VK