Applications of AI and IOT in Renewable Energy provides a future vision of unexplored areas and applications for Artificial Intelligence and Internet of Things in sustainable energy systems. The ideas presented in this book are backed up by original, unpublished technical research results covering topics like smart solar energy systems, intelligent dc motors and energy efficiency study of electric vehicles. In all these areas and more, applications of artificial intelligence methods, including artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above in hybrid systems are included.
This book is designed to assist with developing low cost, smart and efficient solutions for renewable energy systems and is intended for researchers, academics and industrial communities engaged in the study and performance prediction of renewable energy systems.
Author(s): Rabindra Nath Shaw, Ankush Ghosh, Saad Mekhilef, Valentina Emilia Balas
Publisher: Academic Press
Year: 2022
Language: English
Pages: 246
City: London
Front Cover
Applications of AI and IOT in Renewable Energy
Copyright Page
Contents
List of contributors
1. Machine learning algorithms used for short-term PV solar irradiation and temperature forecasting at microgrid
1.1 Introduction
1.2 Proposed work
1.2.1 Overview
1.2.2 Different approaches
1.2.2.1 Linear regression
1.2.2.2 Random forest regression
1.2.2.3 K-nearest neighbor
1.2.2.4 Support vector machines
1.2.3 Forecasting accuracy evaluation and validation
1.2.4 Data creation
1.3 Simulation results and comparison
1.3.1 Recommendation section
1.4 Conclusion
References
2. Generators’ revenue augmentation in highly penetrated renewable M2M coordinated power systems
2.1 Introduction
2.2 Problem formulation
2.2.1 Locational marginal prices expression
2.3 Algorithm
2.4 Interior-point technique and KKT condition
2.4.1 Karush–Kuhn–Tucker conditions
2.4.2 Solution algorithm
2.5 Test results and discussion
2.6 Conclusion
References
3. Intelligent supervisory energy-based speed control for grid-connected tidal renewable energy system for efficiency ma...
3.1 Introduction
3.2 Marine current conversion system modeling
3.2.1 Tidal turbine model
3.2.2 Permanent magnet synchronous generator modeling
3.3 Control of the permanent magnet synchronous generator using passivity method
3.3.1 Permanent magnet synchronous generator dq-model interconnected subsystems decomposition
3.3.2 Permanent magnet synchronous generator passivity property
3.3.3 Workless forces identification
3.3.4 Speed-controlled dq model of the permanent magnet synchronous generator
3.4 Passivity-based speed controller computation
3.4.1 Desired voltage and desired current computation
3.5 Grid-side converter control
3.6 Simulation and experimental results
3.6.1 Performance analysis under fixed parameters
3.6.2 Robustness analysis
3.7 Conclusion
References
4. An intelligent energy management system of hybrid solar/wind/battery power sources integrated in smart DC microgrid fo...
4.1 Introduction
4.2 Mathematical description of the hybrid energy system
4.2.1 Wind system model
4.2.2 Solar power system model
4.2.3 Battery system model
4.2.4 AC grid model
4.2.5 Load side converters model
4.3 Mathematical description of the hybrid energy system
4.3.1 Source-side converters controllers design
4.3.2 Load side converters controller design
4.3.3 Energy management unit
4.4 Numerical results
4.5 Conclusion
References
Further reading
5. IoT in renewable energy generation for conservation of energy using artificial intelligence
5.1 Introduction
5.2 Related work
5.2.1 Internet of things and renewable energy
5.3 Proposed methodology
5.4 Deep Q-learning
5.5 Results analysis and discussion
5.6 Conclusion and future work
References
6. Renewable energy system for industrial internet of things model using fusion-AI
6.1 Introduction
6.1.1 Renewable energy system for smart production
6.1.2 Energy management for renewable energy system
6.1.3 Predictive maintenance
6.2 Related work
6.3 Internet of things in renewable energy sector
6.3.1 Automation to advance complete production
6.3.2 Smart grids for elevated renewable implementation
6.3.3 The internet of things is increasing renewable energy adoption
6.3.3.1 Energy expenditures
6.3.3.2 Balancing supply and demand
6.3.3.3 Cost-effectiveness
6.4 Proposed methodology
6.4.1 Interruption attacks
6.5 Renewable energy system for industrial internet of things model
6.6 Results analysis
6.6.1 Mean absolute error
6.6.2 Mean squared error
6.6.3 Root mean squared logarithmic error
6.6.4 Mean absolute percent error
6.7 Conclusion
Reference
7. Centralized intelligent fault localization approach for renewable energy-based islanded microgrid systems
7.1 Introduction
7.2 Challenges in disturbance detection
7.2.1 Behavior of power electronics converters
7.2.2 Other disturbances and detection challenges
7.3 Requirements for classifier development
7.3.1 Feature extraction
7.3.2 Machine learning
7.4 Centralized fault localization method
7.4.1 Data gathering
7.4.1.1 Feature extraction
7.4.2 Fault/disturbance detection
7.5 Numerical simulations
7.5.1 Data collection
7.5.2 Results and discussion
7.6 Conclusion
References
8. Modeling of electric vehicle charging station using solar photovoltaic system with fuzzy logic controller
8.1 Introduction
8.2 Components of charging station
8.2.1 Solar photovoltaic array
8.2.2 Boost converter
8.2.3 Battery model
8.2.4 Battery charger
8.3 Control systems strategies
8.3.1 Battery charger control system
8.3.2 Photovoltaic array control
3.2.1 Fuzzy logic control
3.2.2 Rules for fuzzy logic controller
8.4 Simulation and result
8.5 Conclusion
References
9. Weather-based solar power generation prediction and anomaly detection
9.1 Introduction
9.1.1 Related work
9.1.2 Contributions
9.2 Prediction of solar power generation
9.2.1 Regression-based power generation prediction
9.2.2 Anomaly in prediction of power generation
9.3 Experiments and results
9.3.1 Data information
9.3.2 Weather-based power generation prediction
9.3.3 Anomaly detection
9.4 Conclusion and future work
References
10. RMSE and MAPE analysis for short-term solar irradiance, solar energy, and load forecasting using a Recurrent Artificial...
10.1 Introduction
10.2 Literature survey
10.2.1 Load forecasting
10.2.2 Solar irradiance forecasting
10.2.3 Solar energy forecasting
10.3 Prediction methodology
10.4 Artificial Neural Network
10.5 Data description
10.6 Key performance indicator
10.7 Results and discussion
10.8 Conclusions
References
11. Study and comparative analysis of perturb and observe (P&O) and fuzzy logic based PV-MPPT algorithms
11.1 Introduction
11.2 Photovoltaic system
11.2.1 Photovoltaic source modeling
11.2.2 DC-DC converter modeling
11.3 Maximum power point tracking system
11.3.1 Perturb and observe based maximum power point tracking system algorithm
11.3.2 Design of fuzzy logic based maximum power point tracking system
11.4 Simulation results and discussion
11.5 Conclusion
References
12. Control strategy for design and performance evaluation of hybrid renewable energy system using neural network controller
12.1 Introduction
12.2 Modeling of hybrid power system
12.3 Control strategy
12.3.1 Neural network model
12.4 Proportional-integral-derivative control and performance index
12.5 Simulation results and discussion
12.6 Conclusıons
References
Index
Back Cover
