
Fault Analysis and its Impact on Grid-connected Photovoltaic Systems Performance
Description
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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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Persons
Ahteshamul Haque, PhD, is an Associate Professor at the Department of Electrical Engineering in Jamia Millia Islamia, a Central University in New Delhi, India. He was awarded the Outstanding Engineer award in 2019 by the IEEE Power & Energy Society.
Saad Mekhilef is a Distinguished Professor at the School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Australia. He is also a Chartered Engineer and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE).
Content
About the Editors
List of Contributors
Preface
Chapter 1: Overview and Impact of Faults in Grid Connected PV systems
Mohammed Ali Khan
Chapter 2: Aging Detection for Capacitors in Power Electronic Converters
Zhaoyang Zhao, Pooya Davari, Huai Wang
Chapter 3: Photovoltaic Module Fault. Part 1: Detection with Image Processing Approaches
V S Bharath Kurukuru, Ahteshamul Haque
Chapter 4: Photovoltaic Module Fault. Part 2: Detection with Quantitative-Model Approach
V S Bharath Kurukuru, Ahteshamul Haque
Chapter 5: Failure Mode Effect Analysis of Power Semiconductors in a Grid Connected Converter
V S Bharath Kurukuru, Irfan Khan
Chapter 6: Fault Classification Approach for Grid-Tied Photovoltaic Plant
V S Bharath Kurukuru, Ahteshamul Haque
Chapter 7: System-Level Condition Monitoring Approach for Fault Detection in Photovoltaic Systems
Zahraoui Younes, Ibrahim Alhamrouni, Barry P. Hayes2, Saad Mekhilef
Chapter 8: Fault Tolerant Converter Design for Photovoltaic System
Azra Malik, Ahteshamul Haque
Chapter 9: IoT based Monitoring and Management for Photovoltaic System
Azra Malik, Ahteshamul Haque, V S Bharath Kurukuru
Index
1
Overview 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 (aß) 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
(1.1)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:
(1.2)Figure 1.1 Overview of grid-connected PV...
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