An Efficient Frequency Estimation Methodology using Genetic Algorithms in FPGA

Abstract

The main objective of this research is to present a fast and efficient method based on Genetic Algorithms (GAs) for measuring the frequency deviation as well as the voltage magnitude and phase angle of a noisy sinusoid wave. The frequency estimation is formulated as an optimization problem, where the goal is to minimize the estimation error. The use of GAs has the advantage of better immunity against noise disturbance present in the input data. In addition, this work investigates the implementation of a GA scheme in FPGA (Field-Programmable Gate Array) in order obtain a device that can work properly in real time. The new approach is able to accurately estimate the frequency, voltage magnitude and phase angle, simulating an online frequency relay. The proposed methodology provides an efficient alternative for frequency relays with advantages in comparison with traditional approaches.I.

Genetic algorithms applied to phasor estimation and frequency tracking in PMU development

“…This generates flexibility to explore parallel computer solutions using FPGAs. In this paper, the parallelism of GAs was explored for frequency estimation using an FPGA [23]. The proposed GA effectively generates several individuals (potential solutions) in parallel, significantly reducing the computing time.…”

“…The details of how to parallelize a GA for frequency estimation is described in [22][23][24]. The resulting hardware is much faster than the sequential GA so that a frequency estimation requires less than a millisecond, enabling its application in real-time.…”

“…El-Naggar and Youssef [21] presents a pioneer publication using GAs in estimation problems for frequency relays. Pursuing realtime applications, an optimized GA was presented in [22,23]. Ref.…”

Algoritmos evolutivos como estimadores de frequência e fase de sinais elétricos: métodos multiobjetivos e paralelização em FPGAs

“…O grupo de pesquisa vem utilizando os AEs como ferramenta, em diversos problemas que necessitam de uma estimação de frequência rápida e confiável, pondendo citar: (Macêdo et al, 2002), (Vargas et al, 2005), (de Souza et al, 2006), (Souza et al, 2007a), (Souza et al, 2007b), , (de Carvalho et al, 2009), (Coury et al, 2009a), (Coury et al, 2009b), (Usida et al, 2010).…”

“…Resultados em (Souza et al, 2007b), (de Souza, 2008), , (Coury et al, 2009a), (Coury et al, 2009b), (Coury et al, 2012) e (Silva, 2013) confirmaram o desempenho dos AEs desenvolvidos neste projeto de pesquisa. Por fim, ressalta-se que o paralelismo necessário foi obtido, tanto no processamento da função, quanto na geração de indivíduos.…”