Reliability of Power Electronics Converters for Solar Photovoltaic Applications
2: Centre of Reliable Power Electronics, Department of Energy Technology, Aalborg University, Aalborg, Denmark
3: Zhejiang University, Hangzhou, China
4: Department of Electrical Engineering, Jamia Millia Islamia University, New Delhi, India
The importance of power electronic converters for electricity grid equipment is increasing due to the growing distribution-level penetration of renewable energy sources. The performance of the converters mostly depends on interactions between sources, loads, and their state of operation. These devices must be operated with safety and stability under normal conditions, fault conditions, overloads, as well as different operation modes. Therefore, enhanced control strategies of power electronic converters are necessary to improve system stability. This book for researchers and practitioners discusses enhanced control strategies, fault and failure mode classification mechanisms, and reliability analysis methods for PV modules, power electronic converters, and grid-connected PV systems, and thermal image-based monitoring. The technologies conveyed serve to improve the reliability and stability of power systems. Life calculation of converters, and case and reliability studies are included as well. The international author team consists of researchers with a range of backgrounds from academia and industry.
Inspec keywords: voltage control; power grids; power generation control; electric current control; solar power stations; invertors; maximum power point trackers; photovoltaic power systems
Other keywords: power generation control; voltage control; power grids; photovoltaic power systems; solar power stations; maximum power point trackers; invertors; electric current control
Subjects: General and management topics; General electrical engineering topics; Power convertors and power supplies to apparatus; Control of electric power systems; Voltage control; Solar power stations and photovoltaic power systems; Power electronics, supply and supervisory circuits; Solar cells and arrays
- Book DOI: 10.1049/PBPO170E
- Chapter DOI: 10.1049/PBPO170E
- ISBN: 9781839531163
- e-ISBN: 9781839531170
- Page count: 287
- Format: PDF
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Front Matter
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1 Power electronics converters for solar PV applications
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The chapter presents a brief introduction about the power electronics converters used for PV applications. Both the DC-DC converters and DC-AC converters are presented in this chapter and different topologies that are applied in PV applications are explained.
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2 Wear-out failure prediction of a PV microinverter
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This chapter assesses and improves the reliability of a photovoltaic (PV) microinverter product by applying two different mission profiles and system-level electrothermal modelling. The system configuration and wear-out analysis process are described in brief before the electrothermal and lifetime models are developed for reliability-critical components in the microinverter, e.g., semiconductor devices and capacitors. Then the mission profiles of two distinct locations, Arizona, USA, and Aalborg, Denmark, are applied to the developed microinverter models, yielding the annual junction hotspot temperature profiles and annually accumulative damages of components. Monte Carlo simulation and Weibull analysis are performed to obtain the system wear-out failure probability. Finally, an advanced multimode control scheme is introduced, and a new long-lifetime electrolytic capacitor is employed in the DC link; leading to a significant reliability improvements of the PV microinverter product.
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3 Reliability analysis methods and tools
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With the rapid integration and installation of more and more PV systems, the reliability of the PV inverter plays a crucial role in the safety, availability, and LCOE of the overall system. To deal with the ever-growing and stringent reliability requirements that the PV inverter must fulfil, a particular emphasis is placed on the accurate and effective reliability analysis of the inverter during the various stages of its development process. Thus PV inverter manufacturers, integrators, and users have a wide array of reliability analysis methods and tools at their disposal.
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4 Grid-connected solar inverter system: a case study
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Generally, overvoltages trigger the protection aspects of the inverter to disconnect from the grid, resulting in islanding operation of the PV system. This further contributes to abrupt voltage fluctuations, load shedding and sudden changes in power flow. Therefore, the distribution networks associated with high penetration PV generation have a risk of power outages and in turn increased maintenance costs. This necessitated the tools and methodologies to quantify reliability of the grid-connected PV systems. These reliability analysis tools and methods serve to evaluate the performance of PV systems and generate reliability indices, which further aid in achieving efficient design at the planning stage and determining the reduced cost and improved benefits at the operational phase. A systematic way to evaluate the reliability of grid-connected PV inverters is then presented in this chapter. The reliability analysis is carried out at the 2.2 MW grid-connected rooftop PV system installed in Jamia Millia Islamia (JMI), New Delhi, India, considering the variations of input power and failure rates of PV system components under ambient conditions. The reliability analysis is carried out for inverters at both string level and central level with the site data as a benchmark. A basic reliability indices computation method is adapted to realize the operation of both inverter system for several risk metrices and quantify their impact on system operation.
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5 Control strategy for grid-connected solar inverters
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As an essential interface between the photovoltaic (PV) panels and the utility grid, solar PV inverters are responsible for converting intermittent solar energy to meet the utility grid requirement, where the inverter output should be synchronized with the grid voltage in terms of phase frequency and amplitude. In addition, considering system cost, conversion efficiency, power quality, and reliability of the grid-connected PV system, the control strategy of solar inverters should be carefully designed. Regarding grid-connected solar inverters, the basic control strategies include a maximum power point tracking (MPPT) algorithm (i.e., increasing efficiency and maximizing the energy harvesting), a DC-link voltage control, and a grid-connected current control (i.e., responsible for the power injection and current quality). In this chapter, the model of PV modules and a few typical MPPT methods are briefly introduced. Then, the DC-link voltage control and grid-connected current control are presented for the single-phase and three-phase solar inverters, respectively.
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6 Control strategy for grid-connected solar inverter for IEC standards
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The rapid evolution of renewable-based power generation has increased the integration of distributed generation (DG) units recently. Increase in power capacity of the DGs and its feeding to the utilities has triggered many concerns. A vast range of fault, i.e., destabilization of grid due to unregulated power injection, can cause a complete grid collapse. Hence, to tackle such issues and create a secure perimeter for grid operation, utility companies along with the governments of different countries revised the grid codes. These grid codes ensure that the fault, such as frequency mismatch, overvoltage, and undervoltage is detected and depending upon the severity of the fault, appropriate action is performed by controlling the inverter to stabilize the anomaly. If the fault tends to persist even after a prescribed fault tolerance limit, the grid is disconnected from the DGs to prevent any further harm to the utilities as well as the DGs. All the important parameters such as operating voltage with fluctuation limits, frequency fluctuation limit, and permissible time up to which the fluctuation can be tolerated, are prescribed by the grid codes. The grid code enables the low-voltage ride-through (LVRT) capability of the DGs by providing set of operating instruction. In this chapter, a comparative analysis between different grid codes focusing on LVRT requirement and islanding criteria is presented along with the analysis of different control techniques proposed for islanding detection and reactive power injection.
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7 Thermal image based monitoring of PV modules and solar inverters
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To increase the reliability of solar PV systems, condition monitoring of PV modules and inverters is needed without interfering the operation of the system. Thermal image-processing-based techniques are non-invasive and simple and can be used remotely with communication technologies. In this chapter, simple algorithms have been demonstrated using intelligent techniques such as CNN and fuzzy logic to analyse the conditions of PV modules and inverter. Simulation results illustrate the efficacy of these algorithms. Since the thermal images are taken in harsh environmental conditions, the reliability of the proposed methods depends on the selection of detection and classification algorithms.
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8 Failure mode classification for grid-connected photovoltaic converters
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This chapter developed a failure mode classification mechanism for condition monitoring of PV inverters. The developed algorithm performed signal pre-processing by DWT for noise removal, feature extraction and region of interest segmentation. The wavelet coefficients associated with the DWT were optimized by a novel approach based on HSA. Various types of features were extracted once the signal processing is completed. The harmony search analysis proved to be very efficient in choosing the best wavelet coefficient depending upon the structure of the signal. The extracted features are assigned towards corresponding classes and randomly divided as training and test data for the purpose of evaluation of the classifier. K-NN is used to classify the fault conditions of PV inverters into normal and faulty status. A five-fold cross validation is performed to measure the performance of the classifier with the input data. On validation, the developed approach depicted a training accuracy of 96.1 percent. Further, critically analysis is established form component-level information-based guidance for ranking failure mechanism.
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Back Matter
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