book

Photovoltaic Systems

Name: Photovoltaic Systems
ISBN: 9781000545890

by K.Mohana Sundaram, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen, P. Pandiyan

March 2022

Intermediate to advanced

150 pages

4h 25m

English

CRC Press

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Cover Page
Half Title Page
Title Page
Copyright Page
Contents
About the Editors
List of Contributors
1 Online Fault Diagnosis and Fault State Classification Methods for PV Systems
Learning Outcome1.1 Introduction1.2 Types of Faults and Its Classifications1.2.1 Open Circuit Faults1.2.2 Short-Circuit Faults1.2.3 Symmetrical Faults1.2.4 Unsymmetrical Faults1.3 Faults in PV Systems1.3.1 Hot Spots in PV Module1.3.2 Delamination of PV Module1.3.3 Discoloration of Encapsulation of PV Module1.3.4 Corrosion1.3.5 Micro Crack, Snail Trail, and Cell Damage1.3.6 Shading Fault1.4 Comparison of Faults1.5 Detection and Diagnosis of Faults in PV1.5.1 Voltage and Current Measurement1.5.2 IV Characteristics Analysis1.5.3 Fault Diagnosis Based on AI Method1.5.4 Statistical and Signal Processing–Based Approach1.5.5 Methods Based on Power Losses1.6 Classification of Fault States in Solar PV Systems1.6.1 Performance Index of Solar PV System1.6.1.1 Performance Ratio (PR)1.6.1.2 Weather-Corrected Performance Ratio (WCPR)1.6.1.3 Linear Regression (LR) and Weather-Corrected Linear Regression (WCLR)1.7 Assessment of Solar Power Plant Resources1.7.1 Clearness Index (CI)1.7.2 Proposed Variable Index (PVI)1.8 Technique for Detecting Outliers in Solar PV Power Plants1.8.1 Estimated Error Matrix (EEM)1.8.2 Estimated Square Error Index (ESEI)1.8.3 Support Vector Machine (SVM)1.8.4 Kernel Function1.9 ConclusionReferences
2 Fault Diagnosis Techniques for Solar Plant Based on Unsupervised Sample Clustering Probabilistic Neural Network Model
Learning Outcome2.1 Introduction: Background and Driving Forces2.1.1 Existing Works of Literature2.2 Theory of Photovoltaic Faults2.2.1 Photovoltaic Minor Fault Condition2.2.2 Photovoltaic Medium Fault Condition2.2.3 Photovoltaic Heavy Fault Condition2.3 Mathematical Modeling of the PV Systems2.4 Materials and Method2.4.1 Probabilistic Neural Network2.4.2 Multiverse Optimization Algorithm (MOA)2.4.3 Social Spider Optimization Algorithm (SSA)2.4.4 Hybrid Multiverse Social Spider Optimization Algorithm (HMSA)2.5 Experimental Analysis of Proposed Hybrid Fault Detection Model2.6 Result and Discussion2.7 ConclusionReferences
3 A Remote Diagnosis Using Variable Fractional Order with Reinforcement Controller for Solar-MPPT Intelligent System
Learning Outcome3.1 Introduction3.2 Solar Performance in Tamilnadu3.3 Proposed PV Array Parameters3.4 Intelligent Controller Implementation3.4.1 Fractional-Order Differentiator3.4.2 Fractional-Order Control MPP Algorithm3.5 Proposed Implementation of Fractional DQN with RLA3.5.1 RLA Formulation for MPP Control3.5.2 Deep RLA Tracking Problem3.6 Algorithm: Fractional Order DQN MPP3.7 Result and Discussion3.8 Comparative Results of Various Output Characteristics at MPP for Different Solar Irradiation at Standard Temperature Conditions3.9 ConclusionReferences

4 Challenges and Opportunities for Predictive Maintenance of Solar Plants
Learning Outcome4.1 Introduction4.2 Solar Plant Operation4.2.1 Electrical System4.2.2 Inverter System4.2.3 Rooftop System4.2.4 Rack System4.2.5 Ground-Mounted System4.2.6 Tracking Mount System4.2.7 Environmental Conditions4.3 Solar Plant Maintenance4.3.1 Predictive Maintenance4.3.2 Classification of Maintenance4.3.2.1 Preventive Maintenance4.3.2.2 Corrective Maintenance4.4 Methods of Preventive Maintenance4.4.1 PV Panel Cooling4.4.2 Transformer-Less Inverter4.4.3 Lightning Protection4.4.4 Computational Inverse Design4.4.5 Machine Learning and Artificial Intelligence–Based Forecasting Methods4.4.6 Smart Remote Monitoring of PV Plant4.4.6.1 Linux RTAI Monitoring System4.4.6.2 ZigBee-Based Monitoring System4.4.6.3 NI-Based Monitoring System4.4.6.4 ARM Processor-Based Monitoring System4.4.6.5 Satellite-Based Monitoring System4.5 ConclusionReferences
5 Machine Learning–Based Predictive Maintenance for Solar Plants for Early Fault Detection and Diagnostics
Learning Outcomes5.1 Introduction5.2 Machine Learning Techniques5.3 PV Plant Model 5.4 PV Predictive Maintenance5.4.1 Manual Diagnostics–Based Predictive Maintenance5.4.2 FMEA-Based Predictive Maintenance5.4.3 Machine Learning and Forecasting Method–Based Predictive Maintenance5.4.4 Real-Time Sensors–Based Predictive Maintenance5.5 Faults Detection and Diagnosis Strategy5.5.1 PV Module Parameters Extraction5.5.2 Validation of the Model5.5.3 Expansion of the Database5.5.4 Using a PNN Classifier to Identify and Diagnose Faults5.6 Anomaly Identification and Predictive Warnings Using Residual Control5.6.1 Making Daily Residuals Calculations5.6.2 Determining Standard Operating Limits5.6.3 Identifying Residual Patterns5.6.4 Creating Predictive Warnings5.6.5 Alert Usage and Decision-Making5.7 ConclusionReferences
6 Optimization Modeling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
Learning Outcomes6.1 Scenario of Solar Energy6.1.1 Present Scenario of Solar Energy in India and Future Scope6.1.2 National Solar Mission6.1.3 Tariff Trend Comparison of Various Fuels Used for Power Generation6.1.4 Cost of Installation of a Rooftop Solar Power Plant: Financial Savings and Reduction in CO2 Emissions6.1.5 Grid-Connected Solar PV System Block Diagram and Cost Requirement6.1.6 Future of Solar Power Generation6.2 Need for Energy Forecasting in PV Plants6.3 Energy Forecasting Types6.3.1 Significance of Energy Forecasting6.3.2 Short-Term Solar Power Forecasting6.3.3 Long-Term Solar Power Forecasting6.3.4 Energetic Models6.4 Techniques Used for Solar Energy Forecasting6.4.1 Optimization Modeling Techniques6.4.2 Machine Learning Techniques6.5 Condition-Based Maintenance in PV Plants6.5.1 Benefits of Condition-Based Maintenance6.6 SummaryReferences
7 Deep Learning–Based Predictive Maintenance of Photovoltaic Panels
Learning Outcomes7.1 Introduction7.2 PV Panel Maintenance7.2.1 Need for PV Panel Maintenance7.2.2 Various PV Maintenance Methods7.3 Predictive Maintenance7.3.1 Necessity7.3.2 Current Approaches and Opportunities7.3.3 Comparison and Suitability of Deep Learning in Predictive Maintenance7.3.4 Predictive Maintenance Process Using Deep Learning7.3.5 Hazards in Predictive Maintenance7.3.6 Advantages of Preventive Maintenance7.3.7 Recommendations and Future Direction7.4 ConclusionReferences
Index

Content preview from Photovoltaic Systems

About the Editors

Dr. K. Mohana Sundaram received a BE in electrical and electronics engineering from University of Madras in 2000, a MTech in high voltage engineering from SASTRA University in 2002, and a PhD degree from Anna University, India in 2014. His research interests include intelligent controllers, power systems, embedded system, and power electronics. He has completed a Rs. 3.1 million project sponsored by DST, government of India. Currently he is working as a professor in the EEE department at KPR Institute of Engineering and Technology, India. He has produced four PhD candidates under his supervision in Anna University, Chennai. ...

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Photovoltaic Systems

by K.Mohana Sundaram, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen, P. Pandiyan

About the Editors

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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