Artificial neural networks applied to the detection of harmonics in the electrical power

• Autores: Cesar Alejandro Fonseca Núñez, Jean Carlo Zamudio La Rosa, Karina Rosas Paredes, Karim Guevara Puente de la Vega, Cesar Beltrán Castañón
• Formato: artículo
• Fecha de Publicación: 2019
• Institución: Universidad Católica de Santa María
• Repositorio: Revistas - Universidad Católica de Santa María
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ucsm.edu.pe:article/186
• Enlace del recurso: https://revistas.ucsm.edu.pe/ojs/index.php/veritas/article/view/186
• Nivel de acceso: acceso abierto
• Materia: Redes Neuronales Artificiales; Generador de Señales Eléctricas; Detección de Armónicos; Matlab y RNAs; Graficas en C#

This study shows the results of investigations to determine harmonics in electrical current through the use of Artificial Neural Networks (ANN) using the methods of Feedforward-Backpropagation through a generator of electrical signals in C# (C Sharp). We studied the causes of current harmonics, what a re its implications in everyday work and filters to attenuate these harmonics. For generation of harmonics, we implemented a transmitter of electrical signals by software, also developed in C# (C Sharp) so as to obtain raw and real data as possible, in order to perform tests for simulating errors in the signal power that occur in real time and then process this data.It was determined that the best method for the detection of harmonics using Artificial Neural Networks is Feedforward — Backpropagation with supervised training in order to handle the input and output to get a better result.This research is based on the best method for determining these harmonics including the processing speed and be able to train the network to efficiently determine the current harmonics. Using Feedforward network model we are using a multilayer model, having Iwo or more layers improves memory and interpolation of points.