Faults in any components (modules, connection lines, converters, inverters, etc.) of photovoltaic (PV) systems (stand-alone, grid-connected or hybrid PV systems) can seriously affect the efficiency, energy yield as well as the security and reliability of the entire PV plant, if not detected and corrected quickly. In addition, if some faults persist (e.g. arc fault, ground fault and line-to-line fault) they can lead to risk of fire. Fault detection and diagnosis (FDD) methods are indispensable for the system reliability, operation at high efficiency, and safety of the PV plant. In this paper, the types and causes of PV systems (PVS) failures are presented, then different methods proposed in literature for FDD of PVS are reviewed and discussed; particularly faults occurring in PV arrays (PVA). Special attention is paid to methods that can accurately detect, localise and classify possible faults occurring in a PVA. The advantages and limits of FDD methods in terms of feasibility, complexity, cost-effectiveness and generalisation capability for large-scale integration are highlighted. Based on the reviewed papers, challenges and recommendations for future research direction are also provided.

Fault detection and diagnosis methods for photovoltaic systems: A review

A review of photovoltaic systems: Design, operation and maintenance

An overview of corrosion defect characterization using active infrared thermography

The rapid development of photovoltaic (PV) technology and the growing number and size of PV power plants require increasingly efficient and intelligent health monitoring strategies to ensure reliable operation and high energy availability. Among the various techniques, Artificial Neural Network (ANN) has exhibited the functional capacity to perform the identification and classification of PV faults. In the present review, a systematic study on the application of ANN and hybridized ANN models for PV fault detection and diagnosis (FDD) is conducted. For each application, the targeted PV faults, the detectable faults, the type and amount of data used, the model configuration and the FDD performance are extracted, and analyzed. The main trends, challenges and prospects for the application of ANN for PV FDD are extracted and presented.

https://hal-centralesupelec.archives-ouvertes.fr/hal-03087601/document

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

https://hal-centralesupelec.archives-ouvertes.fr/hal-01970593/document

PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system

Photovoltaic (PV) solar energy recorded an exponential growth, in worldwide scale, over the last decade. Inevitably, mature PV markets are becoming highly competitive, boosting the need for research and development (R&D) on efficiency and reliability optimization, maintenance and fault diagnosis of key components, such as the PV modules. Indeed, a significant number of studies and technical papers have been published up today, based on an extensive feedback from both laboratory and real (field) investigations of faults and advanced diagnosis applications, especially for crystalline silicon (c-Si) PV modules. Undoubtedly, such experience is of particular interest for current PV plant operators, future investors, maintenance engineers and the R&D sector of PV industry. However, up today, such research, published in the form of reports, technical papers or even books, remains mostly dispersed and unclassified. This paper represents a comprehensive effort to review and highlight recent advances, ongoing research and future prospects, as reported in the literature, on the classification of faults in c-Si PV modules and advanced diagnosis in the field, by means of the increasingly popular method of infrared thermal (IRT) imaging. In particular, the first main part of this paper, reviews the characteristics of the most common fault types of operating PV modules, in terms of electrical and thermal response. Then, the second part gives a thorough review of recently published research, as well as the state-of-the-art, in the fields of IRT-based fault diagnosis and thermal image processing. On the basis of these two individual though supplementary review parts, an overview table is presented, followed by a discussion on the future prospects and challenges, towards the understanding and diagnosis of faults and their propagation in operating PV modules.

Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: A review of research and future challenges

Thermography is a frequently used and appreciated method to detect underperforming Photovoltaic modules in solar power stations. With the review, we give insights on two aspects: (a) are the developed measurement strategies highly efficient (about 1 module s−1) to derive timely answers from the images for operators of multi-Mega Warr peak power stations, and (b) do Photovoltaic stakeholders get answers on the relevance of thermal anomalies for further decisions. Following these questions, the influence of measurement conditions, image and data collection, image evaluation as well as image assessment are discussed. From the literature it is clear that automated image acquisition with manned and unmanned aircrafts allow to capture more than 1 module s−1. This makes it possible to achieve almost identical measurement conditions for the modules; however, it is documented to what extent the increase in speed is achieved at the expense of image resolution. Many image processing tools based on machine learning (ML) have been developed and show the potential for analysis of infrared (IR) images and defect classification. There are different approaches to evaluating IR anomalies in terms of impact on performance, yield or degradation, of individual modules or modules in a string configuration. It is clear that the problem is very complex and multi-layered. On the one hand, information on the electrical interconnection is necessary, and on the other hand, there is a lack of sufficient and suitable data sets to adapt existing computer vision tools to Photovolatics. This is where we see the greatest need for action and further development to increase the expressiveness of IR images for PV stakeholder. We conclude with recommendations to improve the outcome of IR-images and encourage the generation of suitable public data sets of IR-footage for the development of ML tools.

Infrared imaging of photovoltaic modules: a review of the state of the art and future challenges facing gigawatt photovoltaic power stations

Herein, we present results that emphasize the importance of retaining high insulation resistance in polymer backsheets for the reliable operation of photovoltaic installations. By correlating inverter monitoring data, meteorological data, and spectroscopic information from backsheet materials of photovoltaic modules, we derive performance and degradation rates for inverters connected to photovoltaic modules with several backsheet types. The feasibility of evaluating the degradation status of photovoltaic modules in the field through spectroscopic analysis of the water content in backsheet and encapsulant is demonstrated. We show that an increase in water content is a sign for reduced leakage resistance. Ground impedance was measured alongside relative humidity and temperature for three specific backsheet types. Inverters with polyamide and fluorinated coating/polyethylene terephthalate backsheets showed a significant drop in ground impedance at high humidity, RH > 70%. A loss rate of −2 kΩ/day in ground impedance was found for inverters connected to modules with certain fluorinated backsheets. This value is twice as high as for inverters connected to modules with polyamide backsheets. Time series show that the sensitivity to moisture and water ingress increases with time, especially for modules with polyamide and certain fluorinated backsheets. The worst degradation scenario observed had an annual yield loss of 27% due to inverter outages on humid days. About 25% of inverter trip alerts after 8 years of operation included modules with the specific fluorinated backsheet. In general, the study revealed a distinct backsheet impact on ground impedance and operation of inverters and the resulting yield showing an important role of the choice of polymers for reliable and long-lasting photovoltaic systems. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pip.3621

Evolution of inverter ground impedances for PV modules with various backsheet types

In this study, we introduce a weather data independent self-referencing algorithm for performance analyses of PV power stations based on monitoring data. We introduce this method as an alternative to standard performance ratio analyses, in the case that no meteorological data are available. The self-referencing algorithm utilizes the string with maximum output as a reference. The output of other strings is set in relation to the corresponding value of the reference string, resulting in a quantity we call self-referencing ratio SR. We benchmark this ratio against the established performance ratio, which requires solar irradiance data for calculation. Based on a 9 MWp PV power station with 1,719 strings, we show that we are able to quantify, locate, and visualize the performance of each string in the PV power station. About 10% of the strings are underperforming; most of them are located in the bottom rows of the mounting racks. Shading from adjacent mounting racks is a possible explanation for this finding. Furthermore, IR-images and voltage data indicate a frequent occurrence of strings with a reduced number of modules. An underperformance of less than 2% is challenging to detect, due to the uncertainty range of the chosen dataset. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pip.3649

Claudia Buerhop

Papers

Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: A review of research and future challenges

Infrared imaging of photovoltaic modules: a review of the state of the art and future challenges facing gigawatt photovoltaic power stations

Evolution of inverter ground impedances for PV modules with various backsheet types

Assessment of string performance using self‐referencing method in comparison to performance ratio