Sistema de classificação inteligente de cargas elétricas similares e não similares

• Autor(a) principal: Paixão, Aloisio Ramos da
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
• Texto Completo: http://repositorio.ufes.br/handle/10/8428
• topic: Electrical signals; Similar electrical loads; Electrical characteristics; Intelligent classifiers; Cargas elétricas similares; Características elétricas; Classificadores inteligentes; Sistema de Classificação Inteligente; Engenharia/Tecnologia/Gestão; Energia elétrica; Cargas elétricas; Sinais elétricos; 620.9

This research aims to development a system of intelligent classification of similar and non similar electrical loads, using non-intrusive measurement for the acquisition of voltage and current electric signals. Initially an experimental platform containing an arrangement with 4 similar electrical loads, that is, of the same manufacturer and with identical technical specifications, is implemented. Subsequently, an arrangement with 4 non-similar electric loads is used, in order to allow a comparison with the works observed in the recent literature. Six intelligent classifiers are used in the identification process, namely: k-means, Case-Based Reasoning (CBR), CBR+k-means and three Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANN), being one ANN with 4 neurons in the hidden layer (MLP-4) and two ANNs with 8 neurons in the hidden layer (MLP-8 and MLP-8-C30000) that differ only in the number of cycles used as criterion of Learning. The experiments are performed using electrical signals sampled in the frequencies of 6.25kHz, 12.5kHz and 25kHz, in order to verify the influence of the sampling rate on the identification process. The influence of the number of samples used for the tests is also verified. For this, 50, 100 and 150 samples are used for each load configuration. The tests are performed per device (4 electric loads) and per class (24=16 experimental platform operating configurations). It is verified that both sampling rate and number of samples are influenced the performance of the classifiers, opening up possibilities for the development of new works that aim to find optimal configurations involving such parameters. The results obtained for similar electrical loads reached 85.94% of success when identifying a connected device and 73.75% when identifying an arrangement configuration. On the other hand, results with non-similar electrical loads show the compatibility with the results found in the literature, that is, varying between 92.69% and 100% accuracy.