Applied Soft Computing and Embedded System Applications in Solar Energy (Mathematical Engineering, Manufacturing, and Management Sciences)

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgements
Editors
Contributors
Chapter 1 MPPT Control Systems for PV Power Plants
	1.1 Introduction
		1.1.1 Economic Aspects of PV Power Sources
		1.1.2 Major Requirements for PV Facilities Control
		1.1.3 PV Characteristics
		1.1.4 Presence of Single and Multiple Maximum Points
	1.2 MPPT Algorithms
		1.2.1 Deterministic Algorithms
		1.2.2 Fuzzy Logic Control Algorithm
		1.2.3 Artificial Neural Network
		1.2.4 Differential Evolution
			1.2.4.1 Initialization
			1.2.4.2 Mutation
			1.2.4.3 Crossover
	1.3 MPPT Implementation by Electronic Circuits
	1.4 Conclusion
	References
Chapter 2 Relay Coordination Optimization for Solar PV Integrated Grid Using the Water Cycle Optimization Algorithm
	2.1 Introduction
	2.2 Literature Review
	2.3 Modelling Optimal Relay Coordination Model
		2.3.1 Objective Function
		2.3.2 The Constraint for the Overcurrent Relay Coordination Model
	2.4 Implementation of PV in the IEEE 13 Node Test Feeder
	2.5 Water Cycle Algorithms
	2.6 Result and Discussion
	2.7 Conclusion
	References
Chapter 3 Experimental Investigation of Performance of PV Array Topologies under Simulated PSCs
	3.1 Introduction
	3.2 Theoretical Background
		3.2.1 Equivalent Circuit Model of PV Cells
	3.3 Calculation of PV Performance Parameters
		3.3.1 Mismatch Power Loss (ΔP[sub(L)])
	3.4 Experimental Setup and Design of Experiment
	3.5 Array Configuration Studied in the Present Work
		3.5.1 Series Parallel (SP)
		3.5.2 Bridge-link (BL)
		3.5.3 Total Cross-tied (TCT)
	3.6 Simulated Partial Shading Conditions
		3.6.1 No Shading (NS)
		3.6.2 Column Shading (CS)
		3.6.3 Row Shading (RS)
		3.6.4 Diagonal Shading (DS)
		3.6.5 Random Shading Condition
	3.7 Result and Discussion
	3.8 Conclusion
	References
Chapter 4 Artificial Intelligence in PV System
	4.1 Introduction
	4.2 AI Techniques Used in PV Systems
		4.2.1 Ensemble Learning
		4.2.2 Deep Learning
		4.2.3 Convolutional Neural Network (CNN)
		4.2.4 Boltzmann Machine (BM)
		4.2.5 Auto Encoder
		4.2.6 Machine Learning
	4.3 Extreme Learning Machine (ELM)
	4.4 Neural Network AI Techniques
	4.5 Metaheuristics-Based AI Techniques
	4.6 Conclusions
	References
Chapter 5 Thermodynamic Approaches and Techniques for Solar Energy Conversion
	5.1 Introduction
	5.2 Importance of Solar Energy
	5.3 Nature of Solar Energy
	5.4 Thermodynamic Approach
	5.5 Thermal Equilibrium
		5.5.1 Solar Collector (Types and Applications)
		5.5.2 Flat Plate Solar Water Collector
		5.5.3 Evacuated Tube Collector
	5.6 Solar PV Systems and Applications
		5.6.1 Domestic Off-Grid Photovoltaic System
		5.6.2 Non-Domestic Off-Grid Photovoltaic System
		5.6.3 Grid Connecting Photovoltaic Systems
		5.6.4 Centralized Grid-Connected Photovoltaic Systems
	5.7 Applications of Solar Energy
		5.7.1 Solar Batteries
		5.7.2 Solar-Pumping
		5.7.3 Solar Water Heating
	5.8 Conclusion
	References
Chapter 6 Memristive Behavior: Tool for Fault Detection and Repairing Dye Solar Cells
	6.1 Introduction
		6.1.1 Green Energy
		6.1.2 Photovoltaics
			6.1.2.1 PV Cell Behavior and V–I Characteristics
			6.1.2.2 Influence of Temperature on Solar Cell Characteristics
	6.2 Dye-Synthesized Solar Cell
		6.2.1 Overview of Device Physics
		6.2.2 Photogeneration
		6.2.3 Charge Transportation
		6.2.4 Memristor
	6.3 DSSC from Memristive Viewpoint
		6.3.1 DSSC Fault Detection
		6.3.2 DSSC Spice Model
		6.3.3 DSSC Fault Detection and Repair Using Memristor
		6.3.4 Sensing Operation
		6.3.5 K-Segment Sensing and Memristor Resetting
		6.3.6 Configuration and Repair
	6.4 Conclusion
	References
Chapter 7 Development of IoT-Based Data Acquisition System for Real-Time Monitoring of Solar PV System
	7.1 Introduction
	7.2 Literature Review
	7.3 Hardware System Description
	7.4 Modeling of DAS
	7.5 Performance Results and Validation
		7.5.1 Discussion on Observations
	7.6 Conclusion
	References
Chapter 8 Marine Photovoltaics – An IoT-Integrated Approach to Enhance Efficiency
	8.1 Introduction
	8.2 Laws and Regulations in the Maritime Sector
	8.3 Solar Energy Potential
	8.4 Internet of Things (IoT) and the Ocean
	8.5 Floating Photovoltaics
	8.6 Floating Renewable-Powered Buoys
	8.7 Case Study: Catalina Sea Ranch (CSR)
	8.8 Renewable-Powered Marine Vessels
	8.9 Case Study: Smart Containers
	8.10 Efficient Solar-Powered Marine Vessels
	8.11 Conclusion
	References
Chapter 9 Deep Learning Approach towards Solar Energy Forecast
	9.1 Introduction
	9.2 Literature Review
		9.2.1 Storage Energy and Power Cells
		9.2.2 Mining of the Patents
		9.2.3 Latent Dirichlet Allocation
	9.3 Related Work
	9.4 Mathematical Term Techniques
		9.4.1 Moving Average Process
		9.4.2 ARIMA
		9.4.3 SARIMA
		9.4.4 K Nearest Neighbors
		9.4.5 Regression Trees
	9.5 Designing
		9.5.1 Long Short-Term Memories
		9.5.2 Auto STM
		9.5.3 Statistical Forecasting
		9.5.4 Flame Working
	9.6 Model Views
		9.6.1 Deep Learning
		9.6.2 Daily Vertebo
	9.7 Data Processing
		9.7.1 Temporal Weather Grid
		9.7.2 Mutability
		9.7.3 Data Mining with Visualization
	9.8 Cross-Validation
	9.9 Issues Still in Work
		9.9.1 Metrics Error
		9.9.2 Methodology
	9.10 Results Comparison
	9.11 Conclusion
	References
Chapter 10 The Imperative Role of Solar Power Assistance for Embedded Based Climatic Parameters Measurement Systems
	10.1 Introduction
	10.2 Literature Review
	10.3 Comparative Study of Existing Systems Based on Technology and Applications
	10.4 Conclusion
	References
Chapter 11 IoT with Automation Clustering to Detect Power Losses with Energy Consumption and Survey of Defense Machinery against Attacks
	11.1 Introduction
	11.2 Research Method
		11.2.1 Sybil Attack Properties
		11.2.2 Compromised Phase
		11.2.3 Deployment Phase
		11.2.4 Launching Phase
	11.3 Attack Detection in Wireless ad-hoc Network
		11.3.1 Cryptographic Based
		11.3.2 Location Verification Based
		11.3.3 Network Behavior-Based
		11.3.4 Resource Testing
		11.3.5 Trust-Based
	11.4 Discussion, Results and Analysis
		11.4.1 General Detection Issue
		11.4.2 VANET Issue
		11.4.3 Learning-Based Issue
		11.4.4 Centralized vs Decentralized Issue
	11.5 Conclusion
	Conflict of Interest
	References
Index