A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks

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)

The Tachyon Inflationary Models with Exact Mode Functions

We show two analytical solutions of the tachyon inflation for which the spectrum of curvature (density) perturbations can be calculated exactly to linear order, ignoring both gravity and the self-interactions of the tachyon field . The main feature of these solutions is that the spectral indices are independent with scale.Comment: 5 pages, no figure, to appear in Phys. Rev.

Semi-supervised protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data.</p> <p>Results</p> <p>In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions.</p> <p>Conclusion</p> <p>Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.</p

Deployment of AI-based RBF network for photovoltaics fault detection procedure

In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Although, a rich amount of research is available in the field of PV fault detection using ANN, this paper presents a novel methodology based on only two inputs for the training, validating and testing of the Radial Basis Function (RBF) network achieving unprecedented detection accuracy of 98.1%. The proposed methodology goes beyond data normalisation and implements a ‘mapping of inputs’ approach to the data set before exposing it to the network for training. The accuracy of the proposed network is further endorsed through testing of the network in partial shading and overcast conditions

Fault diagnosis in photovoltaic arrays

This paper proposes a simple automatic technique for fault diagnosis in photovoltaic (PV) arrays, based on the analysis of the an omalies observed in the I-V characteristic. Firstly, the I-V characteristic of the PV array is simulated using Matlab/Simscape tool for different faulty conditions; which is experimentally validated by generating different faults on a PV string installed at the Renewable Energy Laboratory of the University of Jijel (Algeria). Subsequently, we compare the I-V characteristic of the PV string under different faults scenarios, in order to identify the anomalies. Finally, six categories are generated: Normal operation, connection fault, connection fault with shadow effect, partial shadow fault, a group of fault which include shadow effect with faults on bypass diode (open circuit bypass diode, inversed bypass diode, shunted bypass diode), and a group of fault which include: bypass diode fault, cell fault, module fault, and shadow effect with shunted by pass diode fault. The results show that the technique can accurately detect and localize faults occurring in the photovoltaic string

Fault detection method for grid-connected photovoltaic plants

In 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 10min on the designed tool. The method has been validated using an experimental database of climatic and electrical parameters regarding a 20kWp 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

Evaluation of epidermal growth factor-related growth factors and receptors and of neoangiogenesis in completely resected stage I-IIIA non-small-cell lung cancer: amphiregulin and microvessel count are independent prognostic indicators of survival.

We have determined the expression of transforming growth factor alpha (TGF alpha), amphiregulin (AR), CRIPTO, the epidermal growth factor receptor (EGFR), erbB-2, erbB-3, and tumor angiogenesis in a series of 195 patients with stage I-IIIA non-small cell lung cancer (NSCLC) treated with radical surgery to define their usefulness as prognostic indicators of survival. A variable degree of specific staining in cancer cells was observed for the three growth factors and for the three growth factor receptors in the majority of NSCLC patients. A statistically significant association between overexpression of TGF alpha, AR, and CRIPTO was observed. Enhanced expression of AR was significantly correlated with enhanced expression of erbB-2 and advanced T-stage. A direct association was also detected for overexpression of TGF alpha and of ErbB-2 off erbB-3, respectively. Sex, tumor size, nodal status, stage, microvessel count, as a measure of neovascularization, and AR overexpression significantly correlated with overall survival at univariate analysis. In a Cox multivariate analysis, the only characteristics with an independent prognostic effect on OAS were microvessel count [relative hazard (RH), 6.61; P < 0.00001), nodal status (RH, 1.59; P = 0.0013), and AR overexpression (RH, 1.72; P = 0.02). These results suggest that evaluation of neoangiogenesis and of certain growth factors, such as AR, can be useful in addition to conventional pathological staging to select high-risk NSCLC patients who may benefit from postsurgical systemic therapies

Detecting hot spots in photovoltaic panels using low-cost thermal cameras

One of the most important challenges to mitigate global climate change is to move towards replacing petroleum-based energy sources. In this idea, non-conventional renewable energy sources such as photovoltaic (PV) solar and wind power are the most used worldwide. In the case of the massification of PV solar generation systems due to its low cost, it has resulted in the use of large-scale supervision techniques that allow a quick and effective determination of the health status of its main components. This study, performs an analysis of the performance of different low-cost cameras for thermography. The analysis compares the accuracy of the thermal images obtained and the error is quantified by means of an image dispersion analysis in each of them. Three-dimensional meshes and contours figures are also made to determine the temperature of a faulty cell. The study shows that the performance obtained with low-cost cameras presents errors below 10% in costs and less than 0.015 USD/pixel. © Springer Nature Switzerland AG 2020.One of the most important challenges to mitigate global climate change is to move towards replacing petroleum-based energy sources. In this idea, non-conventional renewable energy sources such as photovoltaic (PV) solar and wind power are the most used worldwide. In the case of the massification of PV solar generation systems due to its low cost, it has resulted in the use of large-scale supervision techniques that allow a quick and effective determination of the health status of its main components. This study, performs an analysis of the performance of different low-cost cameras for thermography. The analysis compares the accuracy of the thermal images obtained and the error is quantified by means of an image dispersion analysis in each of them. Three-dimensional meshes and contours figures are also made to determine the temperature of a faulty cell. The study shows that the performance obtained with low-cost cameras presents errors below 10% in costs and less than 0.015 USD/pixel. © Springer Nature Switzerland AG 2020.Sori