John A. Tsanakas

Bio: John A. Tsanakas is an academic researcher from Commissariat à l'énergie atomique et aux énergies alternatives. The author has contributed to research in topics: Photovoltaic system & Condition monitoring. The author has an hindex of 11, co-authored 18 publications receiving 459 citations. Previous affiliations of John A. Tsanakas include Centre national de la recherche scientifique & Democritus University of Thrace.

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

Abstract: 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.

Fault diagnosis of photovoltaic modules through image processing and Canny edge detection on field thermographic measurements

Abstract: Today, conventional condition monitoring of installed, operating photovoltaic (PV) modules is mainly based on electrical measurements and performance evaluation. However, such practices exhibit restricted fault-detection ability. This study proposes the use of standard thermal image processing and the Canny edge detection operator as diagnostic tools for module-related faults that lead to hot-spot heating effects. The intended techniques were applied on thermal images of defective PV modules, from several field infrared thermographic measurements conducted during this study. The whole approach provided promising results with the detection of hot-spot formations that were diagnosed to specific defective cells in each inspected module. These evolving hot spots lead to abnormally low performance of the PV modules, a fact that is also validated by the manufacturer's standard electrical tests.

Fault detection and diagnosis methods for photovoltaic systems: A review

Abstract: 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.

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

Abstract: Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

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

Abstract: 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.

Light Unmanned Aerial Vehicles (UAVs) for Cooperative Inspection of PV Plants

Abstract: After a fast photovoltaic (PV) expansion in the past decade supported by many governments in Europe, in this postincentive era, one of the most significant open issues in the PV sector is to find appropriate inspection methods to evaluate real PV plant performance and failures. In this context, PV modules are surely the key components affecting the overall system performance; therefore, there is a main concern about the occurrence of any kind of failure in PV modules. This paper aims to propose a novel concept for monitoring PV plants by using light unmanned aerial vehicles (UAVs) or systems (UASs) during their operation and maintenance. The main objectives of this study are to explore and evaluate the use of different UAV technologies and to propose a reliable, cost-effective, and time-saving method for the inspection of PV plants. In this research, different UAVs were employed to inspect a PV array field. For this purpose, some thermal imaging cameras and a visual camera were chosen as monitoring tools to suitably scan PV modules. The first results show that the procedure of utilizing UAV was effective in the detection of different failures of PV modules. Moreover, such a process was much faster and cost effective than traditional methods.