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This work proposes a novel fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN). For a given set of working conditions - solar irradiance and photovoltaic (PV) module's temperature - a number of attributes such as current, voltage, and number of peaks in the current-voltage (I-V) characteristics of the PV strings are calculated using a simulation model. The simulated attributes are then compared with the ones obtained from the field measurements, leading to the identification of possible faulty operating conditions. Two different algorithms are then developed in order to isolate and identify eight different types of faults. The method has been validated using an experimental database of climatic and electrical parameters from a PV string installed at the Renewable Energy Laboratory (REL) of the University of Jijel (Algeria). The obtained results show that the proposed technique can accurately detect and classify the different faults occurring in a PV array. This work also shows the implementation of the developed method into a Field Programmable Gate Array (FPGA) using a Xilinx System Generator (XSG) and an Integrated Software Environment (ISE)

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic.

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We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.Energies 2019, 12, 1621 2 of 15 measurements and data from weather forecast providers [7] are needed in order to optimize the operation of these systems. The benefits of such models are manifold. Considering the point of view of the owners of residential and Commercial-Industrial (C&I) PV plants, and specifically the possibility of operating in a grid-parity regime or better, accurate forecasts enable the maximization of the self-consumed energy and minimization of the Levelized Cost of the Energy produced (LCoE) [8,9]. Also, when residential and C&I systems operate as part of a microgrid, the power forecasts can be used as input for the Energy Management System (EMS) used to optimize the State of Charge (SoC) of the storage devices [10]. On the other hand, considering larger-scale plants and fuel parity [11], the managers of utility-scale PV plants use the forecasts to optimally plan the downtime of plants for maintenance purposes. Moreover, in countries with a day-ahead electricity market, forecast models allow for optimization of the schedule for the supply offers on the market, avoiding penalties and reduced revenues [12,13]. On the other hand, Distribution System Operators (DSOs) and Transmission System Operators (TSOs) also need reliable forecasts in order to handle the uncertainty and fluctuations of the PV distributed generators connected to their grids. The availability of reliable forecasts allows DSOs and TSOs to cope with the intermittent nature of PV plants, avoiding problems in balancing power generation and load demand [14], enhancing the stability of the system, and reducing the cost of ancillary services [15,16]. Accurate forecasts enhance reliability, and reduce costs by allowing effi...

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Raman spectroscopy was used to characterize the residual stress and defect density of AlN thin films reactively sputtered on silicon (100). The authors studied the correlation between the shift of the E2 (high) phonon of AlN at 658 cm-1 and the film biaxial stress and obtained a biaxial piezospectroscopic coefficient of 3.7 GPa cm-1. A correlation was found between the width of the Raman line, the oxygen concentration measured by secondary ion mass spectroscopy, and acoustic losses. This work lays the basis for the nondestructive assessment of two key thin film properties in microelectromechanical systems applications, namely, acoustic attenuation and residual stress

Forty years ago, Garvie and his Australian co‐workers reported that the stress‐induced transformation of metastable tetragonal zirconia grains to the monoclinic symmetry could give rise to a powerful toughening mechanism. Their results even led them to consider zirconia systems as analogues of certain steels. This seminal paper generated extraordinary excitement in the ceramic community and it is still the subject of extensive research. Transformation toughening is widely used in zirconia materials and results in an increase in strength and toughness when compared to nontransformable ceramics, but the implementation into strong, tough, and sufficiently stable materials has not been fully reached. Zirconia ceramics generally fail at low strains with a much larger scatter in the strength values than metals and require statistical approaches to failure. Here we describe in detail the mechanical behavior laws of ceria‐doped zirconia composites exhibiting a high degree of stress‐induced transformation. They present, to some extent, mechanical behavior analogous to a metal, displaying, (a) significant amount of transformation‐induced plasticity without damage, (b) very high flaw tolerance and (c) almost no dispersion in strength data. They potentially open new application avenues in situations where the advantages of ceramics were dampened by their brittle failure behavior. In particular, the consequences of such behavior for dental implants and additive‐manufactured structures are highlighted.