Estimação das parcelas de contribuição de cargas não lineares na distorção harmônica de tensão de um barramento de interesse do sistema elétrico de potência utilizando rede neural artificial
This work presents a methodology to estimate the non-linear loads contribution on voltage harmonic distortion at a bus of interest in the electric power system. The estimation process is carried out through the development of a model based on artificial neural networks (ANN) added to a sensitivity a...
| Autor principal: | MANITO, Allan Rodrigo Arrifano |
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| Grau: | Tese |
| Idioma: | por |
| Publicado em: |
Universidade Federal do Pará
2019
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| Acesso em linha: |
http://repositorio.ufpa.br/jspui/handle/2011/12042 |
| Resumo: |
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This work presents a methodology to estimate the non-linear loads contribution on voltage harmonic distortion at a bus of interest in the electric power system. The estimation process is carried out through the development of a model based on artificial neural networks (ANN) added to a sensitivity analysis in neural network input. The ANN model input is constituted by the non-linear loads harmonic currents considered in the studied system, and the ANN output corresponds to the harmonic voltage values in the bus under study, for the same harmonic frequency. The study is carried out for each harmonic order individually and the data required for the construction of the model as well as for the results validation have been obtained from synchronized measurement campaigns and by computational simulation, using harmonic load flow studies. Comparisons between reference results through computational simulation with the results obtained by neural model are carried out and it is observed that the developed methodology is able to classify correctly the impact of non-linear loads in the voltage distortion at a bus of interest of the electric system. Additionally, the effectiveness of the methodology is tested in two real systems in order to verify the good performance of this methodology considering real data obtained during measurement campaigns. |