1,721,043 research outputs found
A 24-hours forecast of solar irradiance using artificial neuralnetwork: Application for performance prediction of a grid-connected PV plant at Trieste,Italy
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Explicit empirical model for general photovoltaic devices: Experimental validation at maximum power point
No full textThe validation of a new explicit empirical model for general photovoltaic devices, providing current and voltage at Maximum Power Point (MPP) and current-voltage/power-voltage characteristics under arbitrary conditions of temperature and irradiance, is presented. One of the main advantages of this model is the fact that the equivalent circuit parameters - such as series and shunt resistance, dark-saturation currents, etc. - are not needed, as the sole model input data are the device parameters commonly reported in the datasheets. Moreover, the model is explicit so that its application is very affordable from the computational standpoint. The model is applied to three different types of photovoltaic modules representing some of the most widely diffused technologies in the current market: multi-crystalline silicon, CdTe and CIGS. The calculated voltages, currents and powers at maximum power point are compared with the ones measured for three modules working at the photovoltaic test facility of the University of Trieste. A statistical analysis is presented in order to prove the effectiveness and reliability of the model at maximum power point. Finally, the results of the new explicit model are compared with those obtained by a polynomial regression, Artificial Neural Network (ANN), the well-known single-diode model and an additional, different explicit model. This work shows that the electric performance of a photovoltaic module can be predicted with a high degree of accuracy on the sole basis of parameters that are always found in the photovoltaic device's datashee
Performance prediction of 20kWp grid-connected photovoltaicplant at Trieste (Italy) using artificial neural network
No full textOn-line fault detection of a fuel rod temperature measurement sensor in a nuclear reactor core using ANNs
No full textIn this paper a detailed method for fault detection of an in-core three wires Resistance Temperature
Detectors (RTD) sensor is introduced. The method is mainly based on the dependence of the fuel rod
temperature profile on control rods elevation and coolant flow rate in a given nuclear reactor. For the
implementation, an artificial neural network (ANN) technique has been developed to model the dynamic
behaviour of the considered temperature sensor. In order to have more refined model estimation, ANN
has been combined with additional noise reduction algorithms. The effective denoising work was done
via the discrete wavelet transform (DWT) to remove various kinds of artefacts such as inherent measurement
noise. The principle of the adopted fault detection task is based on the calculation of the
difference between the ANN model estimated temperature and the online being measured temperature
and then compare the deviation with a certain detection threshold to decide the sensor fault. The efficiency
of the method is evaluated first on a simulated case and then on the on-line measurements
obtained from a real plant. Results confirm the capacity of the developed ANN-based model to estimate a
fuel rod temperature with a reasonable accuracy
A hybrid model (SARIMA–SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant
No full textIn this work, a new hybrid model for short-term power forecasting of a grid-connected photovoltaic plant is introduced. The new
model combines two well-known methods: the seasonal auto-regressive integrated moving average method (SARIMA) and the support vector machines method (SVMs). An experimental database of the power produced by a small-scale 20 kWp GCPV plant is used to develop and verify the effectiveness of the proposed model in short-term forecasting. Hourly forecasts of the power produced by the plant were carried out for a few days showing a quite good accuracy. A comparative study has also been introduced showing that the developed hybrid model performs better than both the SARIMA and the SVM model
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Fault detection method for grid-connected photovoltaic plants
No full textIn this work, an automatic fault detection method for grid-connected photovoltaic (GCPV) plants is
presented. The proposed method generates a diagnostic signal which indicates possible faults occurring
in the GCPV plant. In order to determine the location of the fault, the ratio between DC and AC power is
monitored. The software tool developed identifies different types of faults like: fault in a photovoltaic
module, fault in a photovoltaic string, fault in an inverter, and a general fault that may include partial
shading, PV ageing, or MPPT error. In addition to the diagnostic signal, other essential information about
the system can be displayed each 10 min on the designed tool. The method has been validated using an
experimental database of climatic and electrical parameters regarding a 20 kWp GCPV plant installed on
the rooftop of the municipality of Trieste, Italy. The obtained results indicate that the proposed method
can detect and locate correctly different type of faults in both DC and AC sides of the GCPV plant. The
developed software can help users to check possible faults on their systems in real time
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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