Frequency Estimation Using a Genetic Algorithm With Regularization Implemented in FPGAs

This paper proposes an accurate and precise genetic algorithm (GA) for frequency estimation of electrical power system (EPS) signals. The problem of estimating the frequency of a distorted electrical signal is modeled as an optimization problem. A regularization technique is used to guide the GA to a plausible solution from a practical point of view when the signal has significant output distortions. In this approach, the GA using regularization (GAR) enables the balanced investigation of a number of potential solutions, largely exploring the search space from one side, and weighing the suitable solutions according to practical purposes from the other side. The GAR is programmed in a field-programmable gate array (FPGA) device. This is made possible due to 1) the implicit parallelism of FPGAs in computing their instructions and 2) the suitable choice of steps of GARs to explore this parallelism. To evaluate the performance of the proposed method, an EPS was simulated having typical operation conditions. The relatively high robustness exhibited by the GAR to harmonics and noise makes it a promising equipment in the challenge of construction of smart power grids.