Abstract:It is well known that working photovoltaic (PV) plants show several maintenance needs due to wiring and module degradation, mismatches, dust, and PV cell defects and faults. There are a wide range of theoretical studies as well as some laboratory tests that show how these circumstances may affect the PV production. Thus, it is mandatory to evaluate the whole PV plant performance and, then, its payback time, profitability, and environmental impact or carbon footprint. However, very few studies include a systematic procedure to quantify and supervise the real degradation effects and fault impacts on the field. In this paper, the authors first conducted a brief review of the most frequent PV faults and the degradation that can be found under real conditions of operation of PV plants. Then, they proposed and developed an innovative Geographic Information System (GIS) application to locate and supervise them. The designed tool was applied to both a large PV plant of 108 kWp and a small PV plant of 9 kWp installed on a home rooftop. For the large PV plant, 24 strings of PV modules were modelized and introduced into the GIS application and every module in the power plant was studied including voltage, current, power, series and parallel resistances, fill factor, normalized PV curve to standard test conditions (STC), thermography and visual analysis. For the small PV installation three strings of PV panels were studied identically. It must be noted that PV modules in this case included power optimizers. The precision of the study enabled the researchers to locate and supervise up to a third part of every PV cell in the system, which can be adequately georeferenced. The developed tool allows both the researchers and the investors to increase control of the PV plant performance, to lead to better planning of maintenance actuations, and to evaluate several PV module replacement strategies in a preventive maintenance program. The PV faults found include hot spots, snail tracks, ethylene vinyl acetate (EVA) discoloration, PV cell fractures, busbar discoloration, bubbles and Si discoloration.

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Health Services building stock is usually the top energy consumer in the Administrative sector, by a considerable margin. Therefore, energy consumption supervision, prediction, and improvement should be carried out for this group in a preferential manner. Most prior studies in this field have characterized the energy consumption of buildings based on complex simulations, which tend to be limited by modelisation restrictions and assumptions. In this paper, an improved method for the clusterization of buildings based on their electrical energy consumption is proposed and, then, reference profiles are determined by examining the variation of energy consumption over the typical yearly consumption period. The temporary variation has been analyzed by evaluating the temporary evolution of the area consumption index through data mining and statistical clusterization techniques. The proposed methodology has been applied to building stock of the Health Services in the Castilla y León region in Spain, based on three years of historical monthly electrical energy consumption data for over 250 buildings. This building stock consists of hospitals, health centers (with and without emergency services) and a miscellaneous set of administrative and residential buildings. Results reveal five distinct electrical consumption profiles that have been associated with five reference buildings, permitting significant improvement in the demand estimation as compared to merely using the classical energy consumption indicators.

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Solar Photovoltaic (PV) energy generation will be one of the main energy generation technologies in the inner future thanks to the recent and significant reduction of involved costs. One of the most promising and lowest cost technologies is the thin-film PV modules technology. Their currently lower performance than single-Si PV modules is compensated by their lower costs and more aesthetical display, which make them especially attractive for building integration. However, because of the novelty of this technology, very few studies have been conducted to analyse its aging and degradation, which has a significant impact on the operation and maintenance needs and, in the end, in its Levelized Cost of Energy (LCOE). Thus, in this work, a systematic review on this technology and on its more frequent faults and degradation effects is conducted first. Then, a set of Copper Indium Gallium Selenide (CIGS) PV modules has been tested under real outdoor conditions and its degradation has been analysed after 3 years operation. A new procedure for faults location and quantification has been established for this sort of PV modules; and early degradation and aging data are presented and analysed. Results show a significant early degradation of this technology and a fast expansion of the PV faults.

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In order to evaluate a photovoltaic (PV) plant performance, payback time, profitability and environmental impact, an analysis must be made of plant maintenance needs, module and wiring degradation, mismatches and dust effects and PV cell defects and faults. Although a wide range of studies can be found that show the theory and laboratory testing of how these circumstances may affect PV production, very few studies in the field have covered or quantified real degradation effects and faults using a systematic procedure. The authors have therefore reviewed the conditions of PV plants operating in Southern Europe, examining the most frequently found faults and types of degradation, and they look at how novel technologies, such as geographic information system (GIS) applications, can help maintainers, owners, and promoters to supervise and locate damaged PV modules and monitor their evolution and impact on plant working conditions. GIS applications in this field allow the organization of a geo-referenced database of the system, locating and supervising the thirds of each PV cell in the power plant. With this information, investors and maintainers can exert increased control on the PV plant performance and conduct better preventive maintenance measures. The examples given demonstrate that these sorts of applications can be applied both to large PV plants and to domestic installations.