Fault Analysis and its Impact on Grid-connected Photovoltaic Systems Performance E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Fault Analysis and its Impact on Grid-connected Photovoltaic Systems Performance
A thorough and authoritative discussion of how to use fault analysis to prevent grid failures
In Fault Analysis and its Impact on Grid-connected Photovoltaic Systems Performance, a team of distinguished engineers deliver an insightful and concise analysis on how engineers can use fault analysis to estimate and ensure reliability in grid-connected photovoltaic systems. The editors explore how failure data can be used to identify how power electronics-based power systems operate and how they can help to perform risk analysis and reduce the likelihood and frequency of failure.
The book explains how to apply different fault detection techniques—including signal and image processing, fault tolerant approaches—and explores the impact of faults in grid-connected photovoltaic systems. It offers contributions from noted experts in the field and is fully updated to include the latest technologies and approaches. Readers will also find:
- A failure mode effect classification approach for distributed generation systems and their components
- Explanations of advanced machine learning approaches with significant market potential and real-world relevance
- A consideration of the issues pertaining to the integration of power electronics converters with distributed generation systems in grid-connected environments
- Treatments of IoT-based monitoring, ageing detection for capacitors, image and signal processing approaches, and standards for failure modes and criticality analyses
Perfect for manufacturers and engineers working in the power electronics-based power system and smart grid sectors, Fault Analysis and its Impact on Grid- connected Photovoltaic Systems Performance will also earn a place in the libraries of distributed generation companies facing issues in operation and maintenance.
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Table of Contents
Cover
Title Page
Copyright
Preface
List of Contributors
About the Editors
1 Overview and Impact of Faults in Grid‐Connected Photovoltaic Systems
1.1 Introduction
1.2 Grid‐Connected PV System
1.3 Overview of Module Faults
1.4 Overview of Converter Faults
1.5 Detection Strategies for PV System
1.6 Summary
References
2 Aging Detection for Capacitors in Power Electronic Converters
2.1 Introduction
2.2 Laws of Aging for Capacitors
2.3 Physical Model‐based Condition Monitoring
2.4 Data‐Driven‐based Condition Monitoring
2.5 Results and Analysis
2.6 Summary
References
3 Photovoltaic Module Fault. Part 1: Detection with Image Processing Approaches
3.1 Overview
3.2 Background Information
3.3 Fault Classification Approach
3.4 Panel Area Degradation Analysis
3.5 Summary
References
4 Photovoltaic Module Fault. Part 2: Detection with Quantitative‐Model Approach
4.1 Introduction
4.2 Photovoltaic System Characteristics
4.3 Solar Cell Characterization and Modelling
4.4 Power Variations
4.5 Fault Detection Zone Evaluation
4.6 Summary
References
5 Failure Mode Effect Analysis of Power Semiconductors in a Grid‐Connected Converter
5.1 Introduction
5.2 Power Electronics Converters
5.3 Failure Mode Effect Analysis of Power Semiconductors
5.4 Failure Analysis
5.5 Summary
References
6 Fault Classification Approach for Grid‐Tied Photovoltaic Plant
6.1 Introduction
6.2 Solar Power Plants
6.3 Modeling of PV Power Plant and FC System
6.4 Result Evaluation and Discussion
6.5 Summary
References
7 System‐Level Condition Monitoring Approach for Fault Detection in Photovoltaic Systems
7.1 Introduction
7.2 Aging and Degradation Effects of Components on PV System Operation
7.3 Temperature Impact on PV System Operation
7.4 Irradiance Impact on PV System Operation
7.5 Capacitor ESR Impact on PV System Operation
7.6 Data Acquisition for Failure Modes in PV System
7.7 Fault Classifier Development and Monitoring
7.8 Conclusion
References
8 Fault‐Tolerant Converter Design for Photovoltaic System
8.1 Introduction
8.2 Fault Signature Identification and Fault Diagnosis
8.3 Fault Isolation Strategies
8.4 Post‐Fault Reconfiguration Techniques
8.5 Summary
References
9 IoT‐Based Monitoring and Management for Photovoltaic System
9.1 Introduction
9.2 Background Information
9.3 Research Methodology
9.4 Remote PV Monitoring and Controlling
9.5 Summary
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Visual defects related to PV modules [69, 78].
Table 1.2 IGBT failure and causes.
Table 1.3 Possible failure modes, causes, and mechanisms.
Table 1.4 Fault identification in PV modules using thermography.
Table 1.5 Cell failure detection using I–V characteristics.
Table 1.6 Module failure detection using I–V characteristics.
Chapter 2
Table 2.1 Performance comparison of capacitors (++++ superior, + inferior)....
Table 2.2 Typical failure modes, failure mechanisms, and indicators of Al‐C...
Table 2.3 Typical degradation indicators and acquisition methods.
Table 2.4 Derived CM Methods Based on Principle I and Principle II.
Table 2.5 Summary of CM schemes for PV applications
Chapter 3
Table 3.1 Required inspection conditions.
Table 3.2 Training results of different classifiers for same sample data.
Table 3.3 Sample of statistical features.
Table 3.4 Image acquisition device specifications.
Table 3.5 Sample of image feature from PV panel cell.
Table 3.6 Sample of voltage and current degradation with feature change.
Table 3.7 Comparison of the proposed monitoring method with the conventiona...
Table 3.8 Measured power output.
Table 3.9 Result from an estimation of mismatch losses.
Table 3.10 Parameters of the regression analysis.
Table 3.11 Fractional power loss.
Table 3.12 Cell characteristic factor vs. Fill factor.
Chapter 4
Table 4.1 Electrical characteristics of PV inverter topologies.
Table 4.2 Main characteristics of PV inverter topologies.
Table 4.3 Characteristics of a PV panel.
Table 4.4 Healthy cell measurements for resistance correction.
Table 4.5 Resistance correction associated error.
Table 4.6 Maximum power points for the three scenarios.
Table 4.7 Maximum Power Point Variations.
Table 4.8 Average characteristics of the PV system.
Table 4.9 Loss characteristics of the PV system.
Table 4.10 Short‐circuit cells associated power and voltage losses.
Table 4.11 Uncertainty of power and voltage range for the PV panel.
Table 4.12 Number of faults necessary for power loss to fall in the fault c...
Chapter 5
Table 5.1 Potential failure modes and mechanisms of silicon power devices....
Table 5.2 Simulation parameters for generating the normal and failure modes...
Chapter 6
Table 6.1 PV module specifications.
Table 6.2 Inverter specifications.
Table 6.3 Ranges of
Z
...
Table 6.4 Details of faults injected in the actual PV plant.
Chapter 7
Table 7.1 Summary of fault detection techniques reported in the literature....
Table 7.2 The characteristics of encapsulating the EVA material for PV.
Table 7.3 Comprehensive comparison of the existing methodologies.
Chapter 8
Table 8.1 Summary of model‐free fault classification techniques.
Table 8.2 Summary of switch‐level fault‐reconfiguration strategies.
Table 8.3 Summary of switch‐leg level fault‐reconfiguration strategies.
Table 8.4 Summary of module‐level fault‐reconfiguration strategies.
Table 8.5 Summary of system‐level reconfiguration strategies.
List of Illustrations
Chapter 1
Figure 1.1 Overview of grid‐connected PV system.
Figure 1.2 Block diagram of grid‐connected inverter control.
Figure 1.3 Block diagram of the inner current loop for standalone control.
Figure 1.4 Block diagram of the outer current loop for standalone control.
Figure 1.5 A DQ‐based control diagram for inverter control.
Figure 1.6 Poor clamp PV module design.
Figure 1.7 Shading impact on PV curve.
Chapter 2
Figure 2.1 Typical configurations of PV systems with dc‐link capacitors. (a)...
Figure 2.2 Typical capacitance and voltage ranges of Al‐Caps (electrolytic) ...
Figure 2.3 Equivalent circuit model and impedance characteristics of capacit...
Figure 2.4
R
ESR
and
C
variations versus temperature of a new capacitor (Type...
Figure 2.5 Degradation characteristic of a NIPPON snap‐in Al‐Cap (400 V/470 ...
Figure 2.6 Degradation characteristic of a MPPF‐Cap (1100 V/40 μF).
Figure 2.7 Aging laws of Al‐Caps based on weight, temperature, and volume. (...
Figure 2.8 Aging laws of Al‐Caps based on pressure and structure. (a) Intern...
Figure 2.9 Aging laws of MPPF‐Caps based on weight. (a) Average weight gain ...
Figure 2.10 Aging detection procedure for capacitors.
Figure 2.11 Equivalent circuit and phasor diagram of voltage. (a) Equivalent...
Figure 2.12 Equivalent circuits and voltage profiles of RC charge and discha...
Figure 2.13 Implementation examples of derived methods based on Principle I....
Figure 2.14 Implementation examples of derived methods based on Principle II...
Figure 2.15 Data‐processing procedure for condition monitoring.
Figure 2.16 Typical structures of PV systems. (a) Single‐stage configuration...
Figure 2.17 Capacitor current directly measured scheme (1B and 1F). (a) Impl...
Figure 2.18 System operation model‐based scheme (1C and 1G). (a) Implementat...
Figure 2.19 System operation model‐based scheme (1C and 1G). (a) PV boost co...
Figure 2.20 External signal injection‐based method (1D and 1H). (a) CM proce...
Figure 2.21 Single‐phase grid‐connected PV H4 inverter and H5 inverter. (a) ...
Figure 2.22 Different functions of AI in condition monitoring of power elect...
Figure 2.23 Example of support vector machine (SVM)‐based capacitor monitori...
Figure 2.24 Example of convolutional neural network (CNN)‐based capacitor mo...
Figure 2.25 Intelligent algorithm‐based CM of capacitors.
Figure 2.26 Comparison of the Predicted Results and Target Results.
Chapter 3
Figure 3.1 Acquired thermal images are divided into multiple subregions.
Figure 3.2 Results with classifier training. (a) Validation of training stat...
Figure 3.3 Analysis of training accuracy.
Figure 3.4 Fault classification process.
Figure 3.5 Acquired thermal image for testing.
Figure 3.6 Grayscale of the captured thermal image.
Figure 3.7 Canny‐filtered image (binary image).
Figure 3.8 Hough transform applied to the filtered image.
Figure 3.9 Coordinates identification with the filtered image.
Figure 3.10 The proposed health monitoring of solar PV system.
Figure 3.11 Digital representation of solar panel in visual, grayscale, and ...
Figure 3.12 Segmented solar PV module after applying adaptive threshold proc...
Figure 3.13 Segmented solar PV module after the localization approach.
Figure 3.14 Flow chart of the proposed photovoltaic panel area degradation a...
Figure 3.15 Experimental setup for degradation analysis.
Figure 3.16 Visual image of solar PV modules. (a) Healthy panel. (b) Irregul...
Figure 3.17 Thermal image of solar PV modules. (a) Healthy panel. (b) Irregu...
Figure 3.18 Grayscale image of Solar PV modules. (a) Area of healthy PV pane...
Figure 3.19 Measured power output.
Figure 3.20 Relationship between solar irradiance and power output for syste...
Figure 3.21 Results from the regression analysis for system A, system B.
Chapter 4
Figure 4.1 PN‐junction of a PV cell.
Figure 4.2 Different PV inverter topologies. (a) Central. (b) String. (c) Mu...
Figure 4.3 Faults in PV Systems.
Figure 4.4 I–V curve with fault impact representation.
Figure 4.5 Performance ratio diagram.
Figure 4.6 Capture loss analysis decision tree.
Figure 4.7 Residual current detector for internal fault detection.
Figure 4.8 Setup for time‐domain reflectometry in the PV field.
Figure 4.9 Healthy and damaged cell PV panels.
Figure 4.10 Broken cell corrected and non‐corrected I–V curve for
G
= 1000 W...
Figure 4.11 Healthy cell I–V curves.
Figure 4.12 Power‐voltage curves for a healthy cell.
Figure 4.13 Healthy cell with a front broken glass I–V curves.
Figure 4.14 Power–voltage curves for healthy cell with a front broken glass....
Figure 4.15 Broken glass with damaged cell I–V curves.
Figure 4.16 Broken glass with a damaged cell power–voltage curves.
Figure 4.17 Six‐panel PV system with voltage and current monitoring.
Figure 4.18 PV panel I–V curve.
Figure 4.19 Fault decision zones.
Chapter 5
Figure 5.1 Block diagram representation of power electronics converters inte...
Figure 5.2 Power electronic converters used in a single‐phase grid‐connected...
Figure 5.3 AC–AC converter interfaced in a grid‐connected system.
Figure 5.4 Design technologies of power metal‐oxide‐semiconductor field‐effe...
Figure 5.5 Design technologies of power insulated‐gate bipolar transistor.
Figure 5.6 Steps and materials required for packaging of power semiconductor...
Figure 5.7 Discrete component packaging of a transistor.
Figure 5.8 Detailed discrete component packaging cross‐section of a transist...
Figure 5.9 Module packaging of a power semiconductor device.
Figure 5.10 Module packaging cross‐section of a power semiconductor device....
Figure 5.11 Failure modes, mechanisms, and effects analysis.
Figure 5.12 Different failure types at different locations.
Figure 5.13 Parasitic thyristor within an IGBT.
Figure 5.14 Block diagram of proposed failure detection approach.
Figure 5.15 Inverter terminal voltage for normal operation and power module ...
Figure 5.16 Framework for classifier training.
Figure 5.17 Classifier training results for IGBT failure detection. (a) Trul...
Figure 5.18 Inverter terminal voltage for normal operation and degraded powe...
Figure 5.19 Classifier training results for IGBT degradation. (a) Truly and ...
Chapter 6
Figure 6.1 Design of standalone PV power plant.
Figure 6.2 Design of bi‐modal PV power plant.
Figure 6.3 Design of grid‐connected PV power plant.
Figure 6.4 Single line diagram of reference PV power plant.
Figure 6.5 Schematic of the actual and theoretical plant models.
Figure 6.6 Flow chart of FC system flow chart of FC system.
Figure 6.7 Comparison of power outputs of reference and simulated PV plants....
Figure 6.8 Irradiance obtained at 15 minutes intervals on the test day.
Figure 6.9 Temperature obtained at 15 minutes intervals on the test day.
Figure 6.10 DC voltage of theoretical and actual PV plants under the influen...
Figure 6.11 DC power of theoretical and actual PV plants under the influence...
Figure 6.12
Z
score
for string 2 and string 3 affected by the faults.
Figure 6.13
T
score
for string 2 indicating faulty PV modules.
Figure 6.14
P
str
indicating MPPT unit fault.
Chapter 7
Figure 7.1 Global renewable energy generation capacity from 2017 to 2020.
Figure 7.2 Equivalent circuit of the single‐diode PV model.
Figure 7.3 PV panels aging evolution.
Figure 7.4 The configuration of the capacitor system in a single‐phase inver...
Figure 7.5 Classification of faults in PV systems.
Chapter 8
Figure 8.1 Fault‐tolerant design approach for GTPVS.
Figure 8.2 A three‐step process for ensuring reliable and fault‐tolerant ope...
Figure 8.3 A general model‐based fault‐diagnosis approach.
Figure 8.4 A general model‐free fault‐diagnostic technique.
Figure 8.5 Fault isolation strategies proposed for fault‐tolerant operation ...
Figure 8.6 A general neutral‐point‐clamped (NPC) inverter topology.
Figure 8.7 Redundant switch‐leg‐based fault‐reconfiguration technique.
Figure 8.8 A general modular multilevel converter (MMC) topology for fault r...
Figure 8.9 A general CMC topology for fault reconfiguration.
Figure 8.10 Series and Parallel inverter topology for fault‐tolerant operati...
Chapter 9
Figure 9.1 General structure of solar power plant‐monitoring system.
Figure 9.2 Internet of Things (IoT) system.
Figure 9.3 Environmental and physical factors influencing the PV yield.
Figure 9.4 Evolution of the Internet of Things.
Figure 9.5 A typical IoT‐based monitoring system for PV depicting IoT compon...
Figure 9.6 Flowchart for anomaly detection using IoT in a PV system.
Figure 9.7 Proposed approach for distributing renewable energy harnessing.
Figure 9.8 Workflow for power generation and management.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright
Dedication
Preface
List of Contributors
About the Editors
Begin Reading
Index
Wiley End User License Agreement
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Fault Analysis and its Impact on Grid‐connected Photovoltaic Systems Performance
Edited by
Ahteshamul Haque
Senior Member IEEEDepartment of Electrical EngineeringJamia Millia Islamia (A Central University)India
Saad Mekhilef
IEEE FellowSchool of Science, Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne, Australia
Copyright © 2023 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging‐in‐Publication Data
Names: Haque, Ahteshamul, editor. | Mekhilef, Saad, editor.
Title: Fault analysis and its impact on grid‐connected photovoltaic systems
performance / edited by Ahteshamul Haque, Saad Mekhilef.
Description: Hoboken, New Jersey : Wiley, [2023] | Includes bibliographical
references and index.
Identifiers: LCCN 2022040364 (print) | LCCN 2022040365 (ebook) | ISBN
9781119873754 (hardback) | ISBN 9781119873761 (adobe pdf) | ISBN
9781119873778 (epub)
Subjects: LCSH: System failures (Engineering) | Photovoltaic power systems.
Classification: LCC TA169.5 .F38 2023 (print) | LCC TA169.5 (ebook) | DDC
620/.00452–dc23/eng/20221006
LC record available at https://lccn.loc.gov/2022040364
LC ebook record available at https://lccn.loc.gov/2022040365
Cover Design: Wiley
Cover Image: © peterschreiber.media/Shutterstock
I would like to dedicate this book to the Last Prophet of humanity, Prophet Mohammad, who introduced our creator Almighty ALLAH (SWT) to us.
Ahteshamul Haque
I would like to dedicate this book to my wife who supported me during my academic journey.
Saad Mekhilef
Preface
Automatic fault detection with machine learning techniques has been extensively used to assist the decision‐making process during abnormal conditions. However, these approaches were majorly constrained in the fields of medical and image processing‐based applications. This is because of the complexities due to the unavailability and uncertainty of the input data of real‐world and mainly industrial applications. In light of the above observation, this book proposes a failure mode effect classification approach for distributed generation systems and their components operating in a grid‐connected environment, which are widely established around the world. This book specifically adheres to the faults in grid‐connected photovoltaic systems and measures to classify them for achieving proactive monitoring of the system. Generally, failure mechanisms are a critical aspect of determining the reliability of the power system. The failure mechanisms deal with the physical, chemical, and electrical processes through which the failure occurs in the system. Based on the type of failure process, these failure mechanisms can be modelled when appropriate material and environmental information are available. Moreover, with the advancements in machine learning approaches, the failure data along with the modelled mechanisms can be used to identify the operating state of the power system. This helps monitor the operation of the system, perform a risk analysis, estimate the reliability of the product, and reduce the probability that a customer is exposed to a potential failure and/or process problem.
We would like to thank our research team and authors who have contributed to this book. Researchers from Academia who are working in the field of photovoltaic systems and power electronics converters, and distributed generation companies, which are commonly facing issues in operation and maintenance. This book will focus on helping them for developing efficient proactive monitoring approaches for the day‐to‐day operation and maintenance of grid‐connected photovoltaic systems and their components.
The power module‐manufacturing units, photovoltaic module development companies, power electronics converter assembly units, and various testing and certification centers can refer to this book for understanding the issues regarding the integration of power electronics converters with distributed generation systems in grid‐connected environment. The Research and Development units in various organizations can use the book for developing and implementing advanced controllers with photovoltaic systems.
Ahteshamul Haque
Advance Power Electronics Research LabDepartment of Electrical EngineeringJamia Millia Islamia (A Central University)New Delhi
Saad Mekhilef
School of Science, Computing andEngineering TechnologiesSwinburne University of TechnologyHawthorn, VICAustralia
List of Contributors
Ibrahim Alhamrouni
British Malaysian Institute
Universiti Kuala Lumpur
Selangor
Malaysia
Pooya Davari
AAU Energy
Aalborg University
Ahteshamul Haque
Advance Power Electronics Research Lab
Department of Electrical Engineering
Jamia Millia Islamia (A Central University)
New Delhi
India
Barry P. Hayes
School of Engineering and Architecture
University College Cork
Cork
Ireland
Irfan Khan
Clean and Resilient Energy Systems (CARES) Lab
Texas A&M University
Galveston, TX
USA
Mohammed Ali Khan
Department of Electrical Power Engineering
Faculty of Electrical Engineering and Communication
Brno University of Technology
Brno
Czech Republic
V S Bharath Kurukuru
Advance Power Electronics Research Lab
Department of Electrical Engineering
Jamia Millia Islamia (A Central University)
New Delhi
India
Tarmo Korõtko
Department of Electrical Power Engineering and Mechatronics
Tallinn University of Technology
Smart City Center of Excellence (Finest Twins)
Tallinn
Estonia
Azra Malik
Advance Power Electronics Research Lab
Department of Electrical Engineering
Jamia Millia Islamia (A Central University)
New Delhi
India
Saad Mekhilef
School of Science, Computing and Engineering Technologies
Swinburne University of Technology
Hawthorn, VIC
Australia
Huai Wang
AAU Energy
Aalborg University
Zahraoui Younes
Department of Electrical Power Engineering and Mechatronics
Tallinn University of Technology
Smart City Center of Excellence (Finest Twins)
Tallinn
Estonia
Zhaoyang Zhao
Institute of Smart City and Intelligent Transportation
Southwest Jiaotong University
About the Editors
Dr. Ahteshamul Haque is a Senior Member of IEEE. He is working as Associate Professor, Department of Electrical Engineering, Jamia Millia Islamia (A Central University), New Delhi, India. His area of research is power electronics and its applications, control of power electronics converters, intelligent techniques in power electronics. He has authored and co‐authored around 150 publications in journals and proceedings. He has authored 1 book and 17 book chapters. He was working in R&D labs of world‐reputed multinational companies. His inventions are patented, published, and awarded in the United States, Europe, and India. He has established state‐of‐the‐art Advance Power Electronics Research Lab. More than 20 PhD and masters have graduated under his supervision. He serves in the editorial team of IEEE Journal of Emerging and Selected Topics in Power Electronics as an associate guest editor. Dr. Haque has reviewed around 160 research papers of reputed journals.
Dr. Haque has been awarded with Outstanding Engineer Award for the year 2019 by IEEE Power and Energy Society. He is involved in industry consultancy of power electronics converters, solar PV plant design, grid integration, and MPPT design. He has research collaboration with Aalborg University – Denmark, Swinburne University – Australia, and West Florida University – USA.
Prof. Dr. Saad Mekhilef is an IEEE and IET Fellow. He is a distinguished professor at the School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Australia, and an honorary professor at the Department of Electrical Engineering, University of Malaya. He authored and co‐authored more than 500 publications in academic journals and proceedings and 5 books with more than 34 000 citations, and more than 70 PhD students graduated under his supervision. He serves as an editorial board member for many top journals such as IEEE Transaction on Power Electronics, IEEE Open Journal of Industrial Electronics, IET Renewable Power Generation, Journal of Power Electronics, and International Journal of Circuit Theory and Applications.
Prof. Mekhilef has been listed by Thomson Reuters (Clarivate Analytics) as one of the highly cited (world's top 1%) engineering researchers in the world in 2018, 2019, 2020, and 2021. He is actively involved in industrial consultancy for major corporations in the power electronics and renewable energy projects. His research interests include power conversion techniques, control of power converters, maximum power point tracking (MPPT), renewable energy, and energy efficiency.
1Overview and Impact of Faults in Grid‐Connected Photovoltaic Systems
Mohammed Ali Khan
Department of Electrical Power Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
1.1 Introduction
Climate change has made renewable energy more important in recent years. The rapid increase in the share of renewable energy has made it possible to decentralize power generation. It has helped consumers further reduce their energy costs and help utilities meet their ever‐increasing energy needs. To further support the rise, various countries have developed national strategies and initiatives to promote the introduction of renewable energy [1]. To promote sustainable energy projects, the United Nations has agreed on specific Sustainable Development Goals [2]. Even the European Union has promised to reduce greenhouse gas emissions by about 80–95% by 2050 [3]. In 2017, the total installed capacity of solar energy projects exceeded the net installed capacity of coal, gas, and nuclear power combined [4].
Given the projected growth in distributed generation (DG) production, effective coordination, monitoring, and maintenance tools are required to adapt to existing grid infrastructure. This improves the performance of grid‐connected DG systems to ensure stable power generation and optimal energy harvesting. Abnormal behavior on a particular DG can cause an entire power system failure, which can lead to a major blackout. Failure detection and monitoring of photovoltaic (PV) systems with grid connections have been intensively studied [5–7]. If the grid fails, the PV system connected to the grid needs remote island prevention detection. If the DG cannot recover the network from a failure condition via ride‐through, Remote Island Protection protects the DG from the network, providing the necessary security for both the utility and the DG, and avoiding a complete network failure. The main goal of preventive island protection is to keep the power grid running and to prevent accidental islands in the area for security reasons. In case of inconsiderate isolation, general management tends to power the local PCC site [8]. This isolates the mesh area locally and the network is unaware of the isolation that has occurred. As a result, the life of the utility employee working online can be in danger as they can be electrocuted undetected by ordinary management. In the event of an accidental single trip, even improper grounding can cause significant temporary overvoltage due to sudden loss of the load [9]. Most studies in the literature use PV panel voltage (VI) monitoring for troubleshooting, while techniques such as panel thermal imaging and inverter output are monitored to identify faults. However, the time until failure was discovered was one of the major drawbacks of such methods. Therefore, various intelligent error detection methods have been introduced to achieve fast response times [10–13]. Many fuzzy algorithms have been used to identify defects [14, 15]. However, reliance on a specific set of rules can lead to misclassification. In addition, the increasing number of data loggers and data acquisition devices in various parts of DG units actually provides information on system operation [16]. This data essentially processes information about the health of system components and provides a new approach to evaluating the performance of power systems with great economic potential for operation and maintenance. This data essentially processes information about the health of system components and provides a new approach to evaluating the performance of power systems with great economic potential for operation and maintenance. These aspects have prompted research to monitor conditions and increase the output of solar systems [17–19].
If a malfunction is identified, the malfunction must be managed, and an appropriate remedial mechanism must be provided. This can be done by developing a fault ride‐through (FRT) mechanism [20]. In some studies, FRT mechanisms have been discussed using controller switching or other reactive power injection strategies [21–25]. A grid‐connected PV plant is considered to be in an FRT state if certain criteria in the grid code are not met. At the point of common coupling (PCC), monitoring of various parameters such as operating frequency, operating voltage, power factor and reactive power is performed. If the malfunctioning is severe, maintenance must be scheduled more than the FRT limit. And when the fault is corrected by FRT, the fault can be notified through scheduled inspection, so that deep testing can be performed during the inspection period. During maintenance, diesel generators can operate in autonomous mode [26].
It is essential to understand the system in operation and fault associated with the system as the system may witness a small fault out of the wide spectrum of possibilities, and localization of the faults is necessary for faster response and smooth operation [27, 28]. In this chapter a brief about the fault in a grid‐connected PV system is discussed along with it impact on the system and the method to identify such faults.
1.2 Grid‐Connected PV System
The grid‐connected PV system operates in coordination with the operation taking place on the DC and the AC sides of the inverter. The inverter acts as an isolation between the two sides and aims to maintain a constant AC output to the DC input received. The solar panels convert the irradiation into the electrical output. But the output of panel is varying based on the variation taking place in the irradiance and the temperature. To regulate the output of the solar panel a DC‐DC converter is connected to the system which regulates the power generation and extracts the maximum outcome possible from a solar panel by adopting the maximum power point tracking (MPPT) algorithm [29]. The regulated DC is supplied to the inverter and the inverter is controlled by monitoring the voltage and current at PCC and DC link. The inverter presents an AC output with some harmonics. The filter is used after the inverter to reduce the harmonic and make the system.
The control structure of the inverter plays a very important role in controlling the operation and maintaining a stable operation [30]. To achieve symmetrical power transfer from two interconnected power supplies, the intermediate circuit voltage is regulated by the inverter's capacitor feedback internal loop control. The proportional integrator (PI) controller acts as an active current exchange in the network, but by improving the controller's transient response, you can get a reference to the active power in the test bench. In addition, the feedforward controller coordinates reactive current injection into the network. Steady‐state frame current control (αβ) is used in this study because of its fixed current range, low reliance on network impedance, and easy harmonic compensation for low‐frequency components. This also reduces the effect of harmonics on the mains voltage on the current regulator. Resonant integration reduces the effect of line current harmonics present on the current return. Phase‐locked loops (PLLs) are used to filter harmonics from line voltages and extract positive sequences for synchronization.
1.2.1 Inverter Control
For operating the inverter, it is necessary to vary the switching scheme of the power electronics switches involved depending on the load change and unexpected interferences. Inverter control can be divided into two configurations as explained later [31, 32]:
1.2.1.1 Grid‐Connected Inverter Control
From Figure 1.1, it can be realized that Vin is the voltage at the DC link and vg represents the grid voltage. The voltage monitored at PCC is represented by vpcc. The impedance of the grid is denoted by Zg.The summary of the grid‐connected inverter controller is shown in Figure 1.2. On multiplying the sampling current amplitude (I*) along with grid phase angle obtained by phase lock loop (PLL), a reference current (iref) is obtained. The current controller is denoted by Gi(s). Considering the digital control delay [33], GPWM(s) denotes equivalent gain for the inverter. The mathematical expression can be represented as
where a gain for the modulated wave (vm) is represented by KPWM. Inverter bridge voltage is represented by Vinv which is equal to the ratio between DC link voltage Vin and carrier triangular wave Vtri. The delay transfer function for digital control is represented by Gd(s). The sampling of the control system is denoted by Ts. For simplification of analysis Gd(s) is subjected to second order pade approximation [34] as presented as follows:
Figure 1.1 Overview of grid‐connected PV system.
Figure 1.2 Block diagram of grid‐connected inverter control.
The expression of the current controller [35] can be represented as:
where Kp and Ki are proportional and integral gains.
1.2.1.2 Standalone Inverter Control
For controlling a stand‐alone inverter, a dual‐based control loop is implemented [36–38]. Current is regulated using the inner loop, whereas the voltage across the filter capacitor is controlled by the outer loop. The designing of the loops is explained as follows:
Designing of the inner current loop
For designing the inner current loop, the capacitor current (ic) is considered as feedback value. By using the ic as feedback, the system can achieve better performance in case of load variation. The load current is assumed to be zero [39]. The control diagram for the inner loop is illustrated in Figure 1.3.
For digital implementation, one switching period delay (Ts) needs to be considered as until the next switching cycle, the modulation signal will not get updated [40]. The pure switching cycle delay is represented by a delay block (Gd).
As per the small signal analysis in [41], the standalone controller plant can be represented by Gp in Eq. (1.5)
As per the control diagram represented in Figure 1.3, the following equation represents an uncompensated [42] inner loop
where, Gip represents the uncompensated controller plant and Kpwm is the gain value for Pulse width modulation. On applying PI controller for compensation [42] of the inner loop (Gip − c), the expression can be deduced as
where, Kp and Ki are proportional and integral gains for the PI controller.
Designing of the outer current loop
The voltage across the filter capacitor is regulated by the outer control loop. The representation of the outer control loop is present in Figure 1.4.
Figure 1.3 Block diagram of the inner current loop for standalone control.
Figure 1.4 Block diagram of the outer current loop for standalone control.
where, Gv is the compensated outer voltage loop, and C is the filter capacitor.
The presence of a PI controller at the outer loop helps in achieving a satisfactory value of phase margin and also helps in attaining system stability for the uncompensated plants. In case of a dual loop‐compensated system, the crossover frequency must be approximately ten times larger than the grid frequency and lesser when related to the inner loop crossover frequency. The voltage error is tracked and can be eliminated by implementing direct quadrature (DQ) control with a synchronous rotating frame [43]. For DQ rotor frame control, the AC quantity present in the αβ stationary frame acts as a DC quantity as the rotating frequency for the DQ frame is like the fundamental angular frequency for AC quantities [44]. Both current and voltage loops are performed in a synchronous rotor frame for the DQ control method. The benefit of this method is that the controller can be implemented without the generation of β component. Figure 1.5 depicts the DQ‐based controller representation along with state space representation in Eqs. (1.9) and (1.10).
where, Id, Iq, Vd, and Vq are the direct and quadrature current and voltage components respectively, and Vα, Vβ, Iα, and Iβ are the αβ components of the single‐phase voltage and current, respectively.
Figure 1.5 A DQ‐based control diagram for inverter control.
1.3 Overview of Module Faults
An abnormal operation which takes place during the normal operation of the PV module can cause a system failure. Manufacturing defects are believed to be the main cause of instability in the performance of some modules. Some cases of manufacturing defects are single‐crystal and polycrystalline solar cells, which can be observed in the form of striped rings or medium crystalline defects. Mismatches in mixing ratios cause performance degradation due to light, called a manufacturer's failure to PV failure. Due to modular technology failures, amorphous silicon modules are susceptible to light degradation, resulting in a 10–30% reduction in performance during the early stages of installation [45]. Although this deterioration can be retrieved to some degree by thermal annealing [46], it is only applicable in hot summer, and the characteristics of the module deteriorate due to seasonal fluctuations. The module fault can be categorized into an external issue and a manufacturing fault.
1.3.1 External Issue
In addition to manufacturing defects, there are many others that are usually difficult to classify as manufacturing defects or other issues. Some module failures are due to external causes such as shipping errors, clamping, cabling errors, connector errors, and lightning strikes [47]. Damage to the glass cover and laminate of some modules has been observed to be due to impact and vibration during transport. This defect is incompatible with manufacturing defects and is one of the main external causes of module defects. Most transport failures cannot be seen visually or by observing the nominal power. Of the installation issues correlated with modules, clamps are the most common mistake, especially for frameless PV modules, which leads to glass breakage as illustrated in Figure 1.6.
Sharp‐edged clamp designs, narrow clamps, improper placement, and overtightening of the module clamp screws can stress the PV module and lead to breakage. The effects of broken glass can cause corrosion due to the ingress of moisture through cracks, which creates electrical safety issues and degrades performance during long‐term operation. The resulting cracks also lead to hotspots that lead to module overheating [49]. Cables and connectors connect to the PV system and provide electrical connectivity to other components of the PV system, including solar modules and inverters. Connectors are crucial elements and perform a vital role in securing the safety and reliability of power generation and transmission. In addition to various connectors, low‐voltage DC connectors have been widely discussed for use in electric vehicles and solar systems [50]. A connector can show defects because of the different metals associated with them, which causes corrosion when exposed to the atmospheric humidity in combination with gases [51].
Figure 1.6 Poor clamp PV module design.
Source: Köntges et al. [48]/IEA.
Any limitations due to cables or connectors used in solar systems are not considered manufacturing defects when all impacts and consequences are considered. Connector errors are usually caused by improper cable selection or incorrect connections between PV modules and related components. These failures can result in a complete loss of power to the string and cause arcing and fire. The effect of lightning strikes on the DC side is observed as a bypass diode fault in the module [52]. This effect is usually an external cause that leads to subsequent security breaches. Most of these external causes lead to failure of the module due to the open circuit of the shunt diode or the direct effect of lightning.
Most expected failure modes and degradation mechanisms are associated with junction box failures [53], glass breakage [54], connection failures [55], and delamination [56]. Improperly designed junction boxes allow moisture to penetrate and cause corrosion of the junction box terminals. This causes a wiring failure and an internal electrical arc. There is also a potential for soldering failure, i.e. solder joint fatigue and silver (Ag) leaching, in junction boxes. When the soldering point encounters the Ag electrode of the solar cell, it dissolves in the soldering electrode (tin‐lead (Pb − Sn)) and is observed as an Ag3Sn compound. This Ag‐leaching effect causes the thermal expansion to crack the soldered interface and break the connection. The delamination effect occurs due to adhesion contamination or environmental factors, i.e. moisture, humidity, and corrosion in PV module panels. Lamination failure results in optical reflections, which further causes subsequent loss of power generated from the modules. As reported in [56], more than 90% of PV modules can be delaminated. The requirement of adhesion, which is to be met by the manufactures can result in the delamination of the panel [57].
PV back panels deliver reliable operation at high voltages and safeguard electronic components from the severe field condition [58]. Backsheets are made of a variety of materials such as polymers, glass and metal leaf. Backseats are usually made of a very stable laminated structure and UV‐resistant polymer. The material was selected based on the required mechanical strength, cost, and electrical insulation. There have been some serious problems with this structure as glass can break through improper installation or mechanical stress. Despite the concerns, glass/backsheet structures can deliver 2–3% more power than standard backsheet modules. Polymer laminates are the most used backsheet‐building materials. They have multiple layers, which have the effect of separating the interfaces due to high physical and chemical stresses. The only benefit I have seen with polymer laminate breakage is that it does not create immediate safety concerns when delamination occurs. Peeling the backsheet near the junction box is usually a serious problem as it results in an unrestricted junction box. These interruptions lead to failure of the shunt diode connection, which is further complicated by the formation of an unresolved arc across the total system voltage.
1.3.2 Failure Due to Manufacturing Issue
1.3.2.1 Silicon Wafer‐based PV Fault
PV modules based on crystalline silicon wafers occupy a dominant position in the world of PV modules due to their widespread use [59]. The market share of these modules is 95% (as of 2017) [60], which is the most common type of solar cell. Despite their extensive application and use, these modules are susceptible to failures such as potential and light degradation and snail marks. Discoloration of ethylene‐vinyl acetate (EVA) encapsulant was originally observed at the location of the Carrizo Plain in California in the early 1990s and has emerged as a major problem [49, 61]. The developed single crystalline silicon PV modules which use polyvinyl butyral encapsulant and Tedlar/aluminum/Tedlar backsheet structure [62] need a robust electrical insulation layer in the middle of the cell and the film, which causes many protection concerns. Apart from the insulation, the metal leaf also functions as a high‐voltage capacitor, and the disturbance of the electrical insulation on the foil surface charges the foil with the system voltage. The factor affecting the EVA polymer encapsulation degradation was discussed in [63]. Analysis showed that the field was degraded from yellow to dark brown EVA to understand chemical and physical damage. EVA is manufactured with additives such as crosslinking agents, antioxidants, UV absorbers, hindered amine light stabilizers and adhesives. Discoloration results were observed in various samples, and difficulties due to the diffusion of oxygen and acetic acid due to additive reactions were investigated. The origin of the chromophore creates a transparent EVA ring at the edge of the plate‐based cell. In some EVA discoloration scenarios, one cell of the module is observed to be darker than the other. This usually means that the temperature sensitivity of a particular cell is higher than that of an adjacent cell either due to low photocurrent or because the cell is located above a junction box [64]. In severe cases, EVA discoloration is consistent with EVA embrittlement and concomitant dissolved oxygen corrosion [65]. Although most module failures cause complete failure, discoloration and delamination do not lead to failure, but degrade functionality with a very slow rate of degradation of ∼0.5 %/year[66]. There is an overall loss of ∼10% in severe discoloration, which means that EVA discoloration is absurd for a complete failure of the silicon module.
Observations in [67] revealed different types of cracks: multidirectional cracks, diagonal cracks, and cracks parallel and perpendicular to the tire. When a cell cracks, the device cannot be completely detached from the cell, but resistance occurs between the cell device and the number of cycles present in the deformed module. It is stated that cracking criticality and cracking of solar modules result in low power output stability during artificial aging. Experimental analysis was performed on 667 cracked cells in 27 PV modules and the results showed that 50% of the cracks were oriented parallel to the busbar cracks considered critical. Differences in solar module manufacturing processes cause cracks in the cells, especially in the stretching process [68]. Problems with the transportation and installation of PV modules have also been identified as major causes of cell cracking. For several months, [69] investigated PV modules in which snail tracks were formed due to external influences of faulty modules. Detailed microscope images of the discoloration helped in the identification of the problem. The snail trajectory was observed to be mainly located near the edge of the cell or microcracks. Not all microcracks need to change to a snail trajectory, but whenever a trajectory is observed in a module or cell, a microcrack is found in the same location. The optical impression of the footprint is due to the location of the mesh fingers being discolored brown and imprinted on the EVA foil. The early discoloration process due to snail marks is not well documented in the literature. The visual impression of the snail track varies from module to module and destroys cell fragments and cell edges. What the module traces is called the electromechanical degradation process, but it has never been considered a direct cause of power loss.
Potentially induced degradation (PID) [70] is caused by polarization effects. The level of degradation depends on the polarity and the level of potential difference between the photocell and the earth. The PID is also responsible for the durability issue of the module. Typically, PID occurs when a high voltage causes sodium ions to spread out of the glass through encapsulation and accumulate on the cell surface. This leads to surface recombination, lower filling factor, and increased local fractionation [71]. There are two types of PIDs: irreversible and reversible. Irreversible PID is caused by an electrochemical reaction that leads to electro‐corrosion of transparent conductive oxides. Reversible PID, also known as surface polarization, creates a positive charge in the PV cell, resulting in a leakage current. The leakage current value is determined by the PV array grounding configuration. This lowers the power‐generation capacity of the solar cell. The general situation in the development of PID is observed at various levels such as environmental factors, counting factors, systemic factors, and the cellular level. The electrochemical corrosion of the string connection of the cell due to encapsulation leads to deterioration of the PV module [72, 73]. This phenomenon increases the series resistance of the PV module and lowers the parallel resistance [74]. PV cells typically have front and back contacts connected by bus strips to power external circuits. Defects in the strings cause thermal expansion (contraction and mechanical stress), resulting in loss of output power [75]. The electrochemical corrosion [72, 73] of the string connection of the cell due to encapsulation leads to deterioration of the PV module. This phenomenon increases the series resistance of the PV module and lowers the parallel resistance [74]. PV cells typically have front and back contacts connected by bus strips to power external circuits. Defects in the strings cause thermal expansion (contraction and mechanical stress), resulting in loss of output power [75].
1.3.2.2 Thin Film Module Fault
Thin film PV modules have been developed to save material (and therefore cost). These modules have lower conversion efficiency than traditional crystal modules. However, this is offset by low cost (production is less material and usually less technically required) and improved properties at low irradiation. From a material and manufacturing process perspective, the modules are divided into CuInSe2 (CIS), Cu (In, Ga) Se2 (CIGS), CuGaSe2 (CGS) modules, CdTe modules, amorphous and micromorphos silicon modules, etc. Other thin‐film cells such as multi‐junction cells, nanostructured cells, and organic cells. The design process for all the above modules is different as they deal with multiple connections and configurations. However, regardless of the design procedure, the causes and consequences of malfunctioning of a given module have the potential to deteriorate the module. Some of the potential causes and effects observed for thin film modules are spliced connectors, micro arcs, hotspot shunts [76], windshield breakage, and back contact degradation [77]. Initial degradation of 4–7% can be expected within the first 1–3 years depending on climate and system interconnection factors. The various defects that can be viewed without the aid of the tool are shown in Table 1.1.
1.4 Overview of Converter Faults
Grid‐connected PV systems are considered highly reliable. However, like any other complex electrical system, it is vulnerable to failure. These PV systems have a modular structure. Their output can vary from a few watts to megawatts. Therefore, it can have different types of topologies and configurations [79], further complicating the assessment of system failure modes. Researchers have observed that most of the grid‐connected system failure are related to inverters. The operation of the inverter is different from that of a rotating machine such as an induction machine. Their fault currents decay rapidly because they have no inductive characteristics, which are largely dependent on the time constant of the circuit [80]. However, it can be controlled so that the error reaction time can be changed under program control. The inverter can use a voltage or control algorithm depending on the failure that occurs. During the transition period, using the voltage adjustment algorithm will increase the error contribution. With current control algorithms, the rate of increment or decrement is much lower. Inverters are mainly composed of switching devices such as insulated gate bipolar transistors (IGBTs) and metal oxide semiconductor field effect transistors (MOSFETs). Studies show that nearly 34% of power electronics system failures can be traced back to switching devices and solder joint failure [81, 82]. DC link capacitors are also another most susceptible component in systems [83]. There are two types of errors in these switching devices. The first type is a failure that occurs suddenly, such as a sudden overvoltage or overcurrent or a sudden temperature rise. The second type is failure due to gradual wear over a long period of time.
Table 1.1 Visual defects related to PV modules [69, 78].
Fault type
Power loss
Safety issue
Visual fault
Short circuit of wires in module and diode
Power loss
(<3%)
May cause fire hazard
Fragments of cell laminated
Power loss
(<3%)
Can cause electrical shock, fire hazard and physical risk
Cell cracks (
<10%
of cell area)
Power loss, degradation with overtime saturation
No impact on safety of individual
Delamination
Power loss, degradation with step time
Safety concern due to electrical shock
Marks of burning on backsheet
Power loss, degradation with step time
Can cause electrical shock, fire hazard, and physical risk
Discoloration of front panel due to overheating of metallic interconnections
Power loss, degradation with step time
Can cause electrical shock, fire hazard, and physical risk
Delamination of multicrystalline Si module
Power loss, degradation with step time
Physical risk due to failure
Delamination of thin film module
Power loss, degradation with step time
Physical risk due to failure
Glass breakage in thin film modules
Power loss, degradation with step time
Physical risk due to failure
EVA browning
Power loss degradation over linear time
No risk initially but with growth in the defect it may result into fire hazard
Snail trails
Power loss degradation over linear time
May cause fire hazard
Delamination of back sheet
Power loss degradation over linear time
May cause fire hazard
There are DC/AC power electronic converters, i.e. inverters for the flexible and efficient conversion of power from DC to AC. Malfunctions can occur which may cause trouble in system operation. These tasks include maintaining optimal power quality in case of a failure, maintaining reliability, eliminating energy losses that occur, and detecting and determining failures. Therefore, to improve reliability, it is necessary to have a tool to predict the occurrence of failures in advance. Error detection may be delayed, or errors may not be detected, and both events can have devastating outcomes. The system must consist of a fault‐tolerant mechanism to prevent it from shutting down in the case of an unforeseen failure [84].
A failure may occur on the DC side or AC side. Failure on the DC side is caused by a failure in the PV panel, capacitor, or boost DC/DC converter. The AC side is affected by the failure of the inverter and the failure of the filter element. Since PV operates in intermittent environmental conditions, aging can cause material deformation and seriously affect PV performance. Man‐made errors such as wiring errors, careless handling, and manufacturing defects are other possible causes of poor PV performance and can lead to failure events as discussed in Table 1.2. Arc failures, local hotspots, and temperature sensitivity of cell materials are a variety of other possible failure events [85]
