Estimação de componentes harmônicos em sistemas elétricos de potência utilizando redes neurais artificiais tensoriais

• Ano de defesa: 2021
• Autor(a) principal: FERNANDES, Thiago Silva
• Orientador(a): SOUZA, Franciso das Chagas de
• Banca de defesa: SOUZA, Franciso das Chagas de , FONSECA NETO, João Viana da , SANTOS, Walbermak Marques dos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: algoritmo de Widrow-Hoff; rede neural artificial; separabilidade de operadores lineares; tensores; gradiente estocástico; estimação de harmônicas;
• Palavras-chave em Inglês: widrow-Hoff algorithm; Widrow-Hoff algorithm; artificial neural network; separability of linear operators; tensors; stochastic gradient; identification of harmonic.
• Área do conhecimento CNPq: Sistemas Elétricos de Potência; Ciência da Computação
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/3693

The Widrow-Hoff rule is the simplest and most used method to update the synaptic weights of an artificial neural network. This is due to its simplicity of application and parametric robustness, properties of interest in several applications. However, in struc tures with high numbers of parameters, this algorithm has high computational cost and low speed of convergence. In this work, a new rule for updating the synaptic weights of a neural network is presented. The proposed method uses the concepts of linear opera tor separability, a typical property of tensors. Once this property is given, a stochastic gradient algorithm can be derived with a significant increase in the learning speed and a reduction in the computational cost when compared to the Widrow-Hoff algorithm. As the number of neural network parameters increases, the performance difference between the two algorithms is enhanced. Another relevant property of the proposed method is that its learning speed is inversely proportional to the computational cost. Due to the great impact generated by the growth of non-linear loads in the electrical power system, which cause distortions in the voltage and current waveforms, the proposed methodology is applied in harmonic estimation problems.