Estimadores de frequência aplicados a sistemas elétricos de potência

Abstract

The frequency estimation is a problem widely studied in many fields including electric power systems. Several methods have been proposed for this purpose, and most of them perform well when the signal is not distorted by harmonics or noises. This paper presents two new methods based on Artificial Neural Networks for frequency estimation. Both use Clarck s transform to generate a phasor that represent the system s signal. In the first methodology this phasor is normalized and feeds the Generalized Regression Neural Network, that ponders the values. At the end it s obtained a phasor where noisy and harmonics are attenuated. The neural network output is then used to calculate the electrical system frequency. Otherwise, the second methodology uses the Adaptive Linear Neural Network. This work tested also various methodologies of frequency estimation proposed in other knowledge fields such as radar, sonar, communications, biomedicine and aviation however with electrical power systems signals. These methods are: Lavopa (proposed by Lavopa et al. 2007), Quinn (proposed by Quinn, 1994), Jacobsen (proposed by Jacobsen e Kootsookos, 2007), Candan (proposed by Candan, 2011), Macleod (proposed by Macleod, 1998), Aboutanios (proposed by Aboutanios, 2004), Mulgrew (proposed by Aboutanios e Mulgrew, 2005), Ferreira (proposed by Ferreira 2001) e DPLL (proposed by Sithamparanathan, 2008). With the exception of DPLL the remaining methods are based on the Discrete Fourier Transform and seek the spectrum frequency peak to than find the fundamental frequency. The nine methodologies are compared with the proposed methods and with the commonly techniques used or studied for electric power systems. Tests include noisy signals, harmonics, sub-harmonics, frequency variations on step, ramp and sinusoidal, also variations on voltage and phase are considered. The tests also include a simulated signal where a load block is inserted and immediately after removed from the system. At the end a comparison is made between the techniques, been able to point each technique advantage and disadvantage trough the comparison identify the best methods to be applied on electrical power systems.