Search

Photovoltaic (PV) generation systems are susceptible to various types of faults. Our objective is to identify unusual operating conditions in a photovoltaic string using only the voltage and current generated at its terminals. To achieve this, we collected voltage and current samples produced by a PV string consisting of six panels during typical operation and four possible types of faults: full panel shading, partial panel shading, panel short-circuits, and electrical arcs in conductor cables. The first three fault groups were further subdivided into six faults, one for each PV panel, resulting in 20 different operating conditions. We collected samples of the electrical system’s generation under each state in different climatic situations. We used the resulting dataset to train a convolutional neural network (CNN) and a classic machine learning method, K-Nearest Neighbors (KNN). The results showed that the CNN’s ability to learn the characteristics that identify each of the 20 operating conditions resulted in an average accuracy of 95.78%, while the KNN, taking into account previously defined features, achieved an accuracy of 86.34%.

The identification of electrical/electronic loads is one of the Smart Grids (SGs) relevant problems. An accuracy of 100%can be achieved in SGs with non-similar loads. However, to the best of the authors knowledge, this level of accuracy was not described in the literature, considering SGs with similar loads. Therefore, the development of an intelligent classification system of similar electrical/electronic loads is described in this paper, aiming at the achievement of 100% accuracy with minimum parameters of the neural classifiers. An experimental platform containing an arrangement with four high similar loads was implemented. In such arrangement, all possible combinations are realized, that is, from all loads turned on until all turned off, resulting in non-similar and very similar loads, as found in real scenarios. The electrical signals demanded by the connected load arrangements are acquired and submitted to two different neural networks. The first neural network is an Extreme Learning Machine (ELM) classifier, where a Particle Swarm Optimization (PSO) technique is applied to select the best ELM model, whereas the second network is a Convolutional Neural Network (CNN) classifier. The ELM-PSO system is based on pre-processed data because it uses information based on the spectrum amplitude of the demand signals (features), whereas the CNN system is based on the use of the raw data, thus requiring a greater processing capacity when compared to the ELM-PSO. The simulation results show that the proposed CNN based system provides success rates considerably higher than the proposed ELM-PSO system, i.e., 100%of accuracy with 381, 512 network parameters against 85%with 14, 989 parameters of the ELM-PSO-based system.

• Skin testing as an in vivo means of evaluating cell-mediated immune function is extremely valuable. However, problems lie in selecting the appropriate antigens and interpreting the results. The suggestion that diphtheria-tetanus would provide a good antigen in infants and children was evaluated by comparing skin test observations with results of skin biopsy examinations. Thirteen subjects, seven of whom were immunodeficient, were evaluated by skin reactions and dermal biopsy specimens. Fifty percent of the immunodeficient patients had positive skin test reactions but had biopsy specimens that were not consistent with delayed-type hypersensitivity (DTH). Although all normal controls on whom biopsies were done had histologic DTH reactions, two had negative or equivocal skin reactions. Our data suggest, on the contrary, that delayed hypersensitivity testing with this antigen is not a good screening test for disorders of immunity or inflammation.(Am J Dis Child 134:479-483, 1980)

Nowadays, there are several multidetector low-level proportional counters on the market, which are able to measure up to 16 samples simultaneously. Such counters are particularly useful for 210Pb dating, when a core is split into many samples to be analysed. In order to preserve this advantage, a simple radiochemical method should be applied for atmospheric, or excess, 210Pb prior to its determination by beta counting. Starting from a 0.5 M HBr leaching step, a simple chemical separation scheme based on anionic ion exchange was developed. In this method, the lead present in the eluate is precipitated as lead chromate and the 210Pb is determined through the beta counting of 210Bi. The application is discussed of the above chemical separation procedure to marine sediment reference samples, IAEA 368 and IAEA 135, as well as to sediment cores from different environments. Using 5 g of dry sediment sample and 24,000 s counting time, a detection limit of 3 mBq.g-1 can be achieved.