Intro
Committees
Preface
Contents
About the Editors
MPPT-Based a Fuzzy Logic and PO Algorithm for Standalone PV System Under Partial Shading Conditions
1 Introduction
2 System Description
2.1 Mathematical Modeling of PV Module
2.2 Partial Shading Effect
3 Power Conditioning Units
3.1 Boost Converter
3.2 Bidirectional Buck-Boost Converter
3.3 Inverter Circuit
4 MPPT Controller
4.1 Perturb and Observe Algorithm
4.2 Fuzzy Logic Controller
5 Results and Discussion
5.1 System Under Uniform Irradiance
5.2 Extracted Power Under PS Conditions

6 Conclusion
References
Comparison of the Efficiency of ANN Training Algorithms for Tracking the Maximum Power Point of Photovoltaic Field
1 Introduction
2 Modeling of Photovoltaic Panels
3 Maximum Power Point Tracker (MPPT)
4 Artificial Intelligence (AI)
4.1 Artificial Neural Networks (ANN)
4.2 ANN Training Algorithms
5 Configuration of the Proposed Approach
5.1 Proposed ANN Architecture
5.2 Proposed PV System
6 Results and Discussion
6.1 Simulation Results of the Proposed ANN Model
6.2 Efficiency of the ANN Model to Track the MPP
7 Conclusion

4 Forecasting of Photovoltaic Energy Production Using LSTM Neural Network
5 Conclusion
References
Diagnosis and Classification of Photovoltaic Panel Defects Based on a Hybrid Intelligent Method
1 Introduction
2 Defects of a Photovoltaic Field
2.1 Partial Shading
2.2 Short-Circuit Fault
2.3 Open-Circuit Fault
3 Artificial Neural Network (ANN)
4 Particle Swarm Optimization (PSO)
5 PSO-ANN
5.1 Design of the PSO-ANN Hybrid Method
5.2 Methodology
5.3 Result and Discussion
6 Conclusion
References

New Reduced Form Approach and an Efficient Analytical Model for the Prediction of the Five Parameters of PV Generators Under Non-STC Conditions
1 Introduction
2 Single-Diode Modelling
3 Method's Theory
4 Results and Discussion
4.1 Results for KC200GT Under STC
4.2 Results for KC200GT Under Non-STC
5 Conclusion
References
Parameter Prediction of Solar Cell's Double Diode Model Using Neural Network
1 Introduction
2 Solar Cell's Double Diode Model
3 Parameter Prediction for Different Values of G and T
4 Artificial Neural Network for Parameter Prediction
4.1 Feedforward Artificial Neural Network Model.