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Photovoltaics Lifetime Output Improvement: Advanced Monitoring, Failure Detection and Classification and Energy Forecasting

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 40535

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Prof. Dr. George E. Georghiou

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FOSS Research Centre for Sustainable Energy, PV Technology Laboratory, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus
Interests: photovoltaics; microgrids; predictive maintenance; data quality; nanogrids; storage systems
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Dr. George Makrides

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PV Technology Laboratory, FOSS Research Center for Sustainable Energy, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus
Interests: photovoltaics; microgrids; predictive maintenance; data quality; nanogrids; storage systems
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Dr. Marios Theristis

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Photovoltaic Technology Laboratory, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus
Interests: solar energy; photovoltaics; performance; failure diagnosis; reliability; forecasting; concentrating photovoltaics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced monitoring techniques and protocols can significantly improve and ensure the quality of operation of grid-connected photovoltaic (PV) systems, hence directly exerting a positive impact on the investment cost, levelized cost of energy (LCOE) and, in general, PV competitiveness. This can be achieved by improving the reliability and service lifetime performance through advanced monitoring, enhanced data analytic methods, early failure detection and classification, degradation rate estimation, and accurate PV production forecasting in different climates.

The aim of this Special Issue is to solicit original and high-quality research articles related to the aforementioned topics. In particular, topics of interest include, but are not limited to:

Advanced monitoring of grid-connected photovoltaic systems
Enhanced data analytic methods for PV monitoring
Degradation rate estimation procedures
Failure detection and classification techniques for grid-connected PV systems
Day- and hour-ahead PV production forecasting
Modelling of PV systems incorporating storage
Reliability modelling of PV systems

Prof. George E. Georghiou
Dr. George Makrides
Dr. Marios Theristis
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

photovoltaic (PV)
PV monitoring
failure detection and classification
energy forecasting

Published Papers (9 papers)

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Research

13 pages, 2299 KiB

Open AccessArticle

Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques

by Ngoc Thien Le and Watit Benjapolakul

Energies 2019, 12(16), 3158; https://doi.org/10.3390/en12163158 - 16 Aug 2019

Cited by 9 | Viewed by 3399

Abstract

Rooftop photovoltaics (PV) systems are attracting residential customers due to their renewable energy contribution to houses and to green cities. However, customers also need a comprehensive understanding of system design configuration and the related energy return from the system in order to support their PV investment. In this study, the rooftop PV systems from many high-volume installed PV systems countries and regions were collected to evaluate the lifetime energy yield of these systems based on machine learning techniques. Then, we obtained an association between the lifetime energy yield and technical configuration details of PV such as rated solar panel power, number of panels, rated inverter power, and number of inverters. Our findings reveal that the variability of PV lifetime energy is partly explained by the difference in PV system configuration. Indeed, our machine learning model can explain approximately

31 %

(

95 %

confidence interval: 29–38%) of the variant energy efficiency of the PV system, given the configuration and components of the PV system. Our study has contributed useful knowledge to support the planning and design of a rooftop PV system such as PV financial modeling and PV investment decision. Full article

(This article belongs to the Special Issue Photovoltaics Lifetime Output Improvement: Advanced Monitoring, Failure Detection and Classification and Energy Forecasting)

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20 pages, 16042 KiB

Open AccessArticle

Assessment of the Impact of Stagnation Temperatures in Receiver Prototypes of C-PVT Collectors

by João Gomes

Energies 2019, 12(15), 2967; https://doi.org/10.3390/en12152967 - 01 Aug 2019

Cited by 1 | Viewed by 2696

Abstract

Concentrating Photovoltaic Thermal (C-PVT) solar collectors produce both thermal and electric power from the same area while concentrating sunlight. This paper studies a C-PVT design where strings of series-connected solar cells are encapsulated with silicone in an aluminium receiver, inside of which the heat transfer fluid flows, and presents an evaluation on structural integrity and performance, after reaching stagnation temperatures. Eight test receivers were made, in which the following properties were varied: Size of the PV cells, type of silicone used to encapsulate the cells, existence of a strain relief between the cells, size of the gap between cells, and type of cell soldering (line or point). The test receivers were placed eight times in an oven for one hour at eight different monitored temperatures. The temperature of the last round was set at 220 °C, which exceeds the highest temperature the panel design reaches. Before and after each round in the oven, the following tests were conducted to the receivers: Electroluminescence (EL) test, IV-curve tracing, diode function, and visual inspection. The test results showed that the receivers made with the transparent silicone and strain relief between cells experienced less microcracks and lower power degradation. No prototype test receiver lost more than 30% of its initial power, despite some receivers displaying a large number of cell cracks. The transparent and more elastic silicone is better at protecting the solar cells from the mechanical stress of thermal expansion than the compared silicone alternative, which was stiffer. As expected, larger cells are more prone to develop microcracks after exposure to thermal stress. Additionally, existing microcracks tend to grow in size relatively fast under thermal stress. EL imaging taken during our experiment leads us to conclude that it is far more likely for existing cracks to expand than for new cracks to appear. Full article

(This article belongs to the Special Issue Photovoltaics Lifetime Output Improvement: Advanced Monitoring, Failure Detection and Classification and Energy Forecasting)

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14 pages, 11634 KiB

Open AccessArticle

Developing Inspection Methodology of Solar Energy Plants by Thermal Infrared Sensor on Board Unmanned Aerial Vehicles

by Dong Ho Lee and Jong Hwa Park

Energies 2019, 12(15), 2928; https://doi.org/10.3390/en12152928 - 30 Jul 2019

Cited by 40 | Viewed by 5857

Abstract

Photovoltaic (PV) power generation facilities have been built on various scales due to rapid growth in response to demand for renewable energy. Facilities built on diverse terrain and on such a scale are required to employ fast and accurate monitoring technology for stable electrical production and maintenance. The purpose of this study was to develop a technology to analyze the normal operation and failure of solar modules by acquiring images by attaching optical and thermal infrared sensors to unmanned aerial vehicles (UAVs) and producing orthographic images of temperature information. The results obtained in this study are as follows: (1) a method of using optical and thermal infrared sensors with different resolutions at the same time is able to produce accurate spatial information, (2) it is possible to produce orthographic images of thermal infrared images, (3) the analysis of the temperature fluctuation characteristics of the solar panel and cell showed that the abnormal module and cell displayed a larger temperature change than the normal module and cell, and (4) the abnormal heat generation of the panel and cell can be accurately discerned by the abnormal state panel and cell through the spatial distribution of the temperature. It is concluded that the inspection method of the solar module using the obtained UAV-based thermal infrared sensor can be useful for safety inspection and monitoring of the rapidly growing solar power generation facility. Full article

(This article belongs to the Special Issue Photovoltaics Lifetime Output Improvement: Advanced Monitoring, Failure Detection and Classification and Energy Forecasting)

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18 pages, 3759 KiB

Open AccessArticle

A Time-Series Data Analysis Methodology for Effective Monitoring of Partially Shaded Photovoltaic Systems

by Odysseas Tsafarakis, Kostas Sinapis and Wilfried G. J. H. M. van Sark

Energies 2019, 12(9), 1722; https://doi.org/10.3390/en12091722 - 07 May 2019

Cited by 9 | Viewed by 3457

Abstract

The majority of photovoltaic (PV) systems in the Netherlands are small scale, and installed on residential and commercial rooftops, where different objects in many cases may lead to the presence of shading and inevitable energy loss. Nevertheless, the energy loss due to expected shadow must be distinguished from the energy loss due to other malfunctions. In this study an algorithmic tool is presented that automates the process of analyzing monitoring data of partially shaded PV systems. The algorithm compares long-term and high-resolution yield data of a partially shaded PV system with the yield data of an unshaded PV system, as reference PV system, and automatically detects the energy loss due to the expected shadow, caused by any surrounding obstacles, and distinguishes it from any additional energy loss due to other malfunctions. This study focuses on PV systems with module-level power electronics (MLPE) since these are mostly used on PV systems on rooftops. Three different cases of shaded MLPE PV systems are presented to illustrate the versatility of the methodology. Furthermore, suggestions for further research are discussed at the end of the paper. Full article

(This article belongs to the Special Issue Photovoltaics Lifetime Output Improvement: Advanced Monitoring, Failure Detection and Classification and Energy Forecasting)

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16 pages, 4652 KiB

Open AccessArticle

Reconfiguration Method to Extract More Power from Partially Shaded Photovoltaic Arrays with Series-Parallel Topology

by Xiaoguang Liu and Yuefeng Wang

Energies 2019, 12(8), 1439; https://doi.org/10.3390/en12081439 - 15 Apr 2019

Cited by 13 | Viewed by 2817

Abstract

A photovoltaic (PV) array is composed of several panels connected in series-parallel topology in most actual applications. However, partial shading of a PV array can dramatically reduce power generation. This paper presents a new reconfiguration method to extract more power from PV arrays under partial shade conditions. The method is designed using the effective maximum power point current and voltage of a PV panel. Its advantages involve (i) the method reconfigures the PV array without measuring the irradiance profile, and (ii) the reconfiguration is executed on the level of a PV module. Based on these two aspects, the method disperses the shade uniformly within the PV array, reducing the mismatch loss significantly and increasing power generation. The performance of the proposed method is investigated for different shade patterns and results show improved performance under partial shade conditions. Full article

(This article belongs to the Special Issue Photovoltaics Lifetime Output Improvement: Advanced Monitoring, Failure Detection and Classification and Energy Forecasting)

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18 pages, 2328 KiB

Open AccessArticle

Reliability, Availability and Maintainability Analysis for Grid-Connected Solar Photovoltaic Systems

by A. Sayed, M. El-Shimy, M. El-Metwally and M. Elshahed

Energies 2019, 12(7), 1213; https://doi.org/10.3390/en12071213 - 28 Mar 2019

Cited by 88 | Viewed by 7574

Abstract

Recently, solar power generation is significantly contributed to growing renewable sources of electricity all over the world. The reliability and availability improvement of solar photovoltaic (PV) systems has become a critical area of interest for researchers. Reliability, availability, and maintainability (RAM) is an engineering tool used to address operational and safety issues of systems. It aims to identify the weakest areas of a system which will improve the overall system reliability. In this paper, RAM analysis of grid-connected solar-PV system is presented. Elaborate RAM analysis of these systems is presented starting from the sub-assembly level to the subsystem level, then the overall system. Further, an improved Reliability Block Diagram is presented to estimate the RAM performance of seven practical grid-connected solar-PV systems. The required input data are obtained from worldwide databases of failures, and repair of various subassemblies comprising various meteorological conditions. A novel approach is also presented in order to estimate the best probability density function for each sub-assembly. The monitoring of the critical subassemblies of a PV system will increase the possibility not only for improving the availability of the system, but also to optimize the maintenance costs. Additionally, it will inform the operators about the status of the various subsystems of the system. Full article

(This article belongs to the Special Issue Photovoltaics Lifetime Output Improvement: Advanced Monitoring, Failure Detection and Classification and Energy Forecasting)

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20 pages, 5169 KiB

Open AccessArticle

Measurement of Thermal and Electrical Parameters in Photovoltaic Systems for Predictive and Cross-Correlated Monitorization

by Carlos Toledo, Lucía Serrano-Lujan, Jose Abad, Antonio Lampitelli and Antonio Urbina

Energies 2019, 12(4), 668; https://doi.org/10.3390/en12040668 - 19 Feb 2019

Cited by 15 | Viewed by 4155

Abstract

Photovoltaic electricity generation is growing at an almost exponential rate worldwide, reaching 400 GW_p of installed capacity in 2018. Different types of installations, ranging from small building integrated systems to large plants, require different maintenance strategies, including strategies for monitorization and data processing. In this article, we present three case studies at different scales (from hundreds of W_p to a 2.1 MW_p plant), where automated parameter monitorization and data analysis has been carried out, aiming to detect failures and provide recommendations for optimum maintenance procedures. For larger systems, the data collected by the inverters provides the best source of information, and the cross-correlated analysis which uses these data is the best strategy to detect failures in module strings and failures in the inverters themselves (an average of 32.2% of inverters with failures was found after ten years of operation). In regards to determining which module is failing, the analysis of thermographic images is reliable and allows the detection of the failed module within the string (up to 1.5% for grave failures and 9.1% of medium failures for the solar plant after eleven years of activity). Photovoltaic (PV) systems at different scales require different methods for monitorization: Medium and large systems depend on inverter automated data acquisition, which can be complemented with thermographic images. Nevertheless, if the purpose of the monitorization is to obtain detailed information about the degradation processes of the solar cells, it becomes necessary to measure the environmental (irradiance and ambient temperature), thermal and electrical parameters (I-V characterization) of the modules and compare the experimental data with the modelling results. This is only achievable in small systems. Full article

(This article belongs to the Special Issue Photovoltaics Lifetime Output Improvement: Advanced Monitoring, Failure Detection and Classification and Energy Forecasting)

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22 pages, 2338 KiB

Open AccessArticle

Complete Procedure for the Economic, Financial and Cost-Competitiveness of Photovoltaic Systems with Self-Consumption

by D. L. Talavera, E. Muñoz-Cerón, J. de la Casa, D. Lozano-Arjona, M. Theristis and P. J. Pérez-Higueras

Energies 2019, 12(3), 345; https://doi.org/10.3390/en12030345 - 23 Jan 2019

Cited by 17 | Viewed by 3650

Abstract

Nowadays, the integration of photovoltaic (PV) systems into the grid involves new and competitive ways to realize this. Thus, it is necessary to define procedures that not only include energy calculations but also incorporate economic and funding feasibility features. According to the literature review, there are numerous tools that are available to carry out a profitability analysis of a photovoltaic system. However, certain shortcomings have been identified, either in the definition of the economic and financial scenarios or in the results obtained, as they do not provide all the necessary information, do not use all the most common economic criteria, or in some cases the complexity and training requirements for their correct implementation may discourage their use. Therefore, in this paper a complete procedure that can be used as a preliminary decision tool prior to the design of an in-depth PV self-consumption system is proposed. Realistic input data makes it possible to not only obtain results for common economic and financial feasibility criteria (Net Present Value, Internal Rate of Return, Discounted Pay-Back Time and Net Cash Balance), but it also allow for a cost-competitiveness evaluation based on the Levelised Cost of Electricity (LCOE). The novel concept of the direct cost of PV self-consumed electricity is also introduced. Full article

(This article belongs to the Special Issue Photovoltaics Lifetime Output Improvement: Advanced Monitoring, Failure Detection and Classification and Energy Forecasting)

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19 pages, 7397 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Analysis of the Performance of Various PV Module Technologies in Peru

by Irene Romero-Fiances, Emilio Muñoz-Cerón, Rafael Espinoza-Paredes, Gustavo Nofuentes and Juan De la Casa

Energies 2019, 12(1), 186; https://doi.org/10.3390/en12010186 - 08 Jan 2019

Cited by 43 | Viewed by 5761

Abstract

A knowledge gap exists about the actual behavior of PV grid-connected systems (PVGCS) using various PV technologies in Peru. This paper presents the results of an over three-year-long performance evaluation of a 3.3-kWp monocrystalline silicon (sc-Si) PVGCS located in Arequipa, a 3.3-kWp sc-Si PVGCS located in Tacna, and a 3-kWp policrystalline (mc-Si) PVGCS located in Lima. An assessment of the performance of a 3.5-kWp amorphous silicon/crystalline silicon hetero-junction (a-Si/µc-Si) PVGCS during over one and a half years of being in Lima is also presented. The annual final yields obtained lie within 1770–1992 kWh/kW, 1505–1540 kWh/kW, and 736–833 kWh/kW for Arequipa, Tacna, and Lima, respectively, while the annual PV array energy yield achieved by a-Si/µc-Si is 1338 kWh/kW. The annual performance ratio stays in the vicinity of 0.83 for sc-Si in Arequipa and Tacna while this parameter ranges from 0.70 to 0.77 for mc-Si in Lima. An outstanding DC annual performance ratio of 0.97 is found for a-Si/µc-Si in the latter site. The use of sc-Si and presumably, mc-Si PV modules in desert climates, such as that of Arequipa and Tacna, is encouraged. However, sc-Si and presumably, mc-Si-technologies experience remarkable temperature and low irradiance losses in Lima. By contrast, a-Si/µc-Si PV modules perform much better in the latter site thanks to being less influenced by both temperature and low light levels. Full article

(This article belongs to the Special Issue Photovoltaics Lifetime Output Improvement: Advanced Monitoring, Failure Detection and Classification and Energy Forecasting)

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