Fast and Efficient Method for Frequency Deviation Measurement
A Fast and Efficient Method for Frequency Deviation Measurement Based on Genetic Algorithms using a FPGA Approach
The main objetive 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 situation was formulated as an optimization problem, and the goal was 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 also investigates the implementation of such a scheme in FPGA (FieldProgrammable Gate Array). It is expected that the new approach is able to accurately estimate the frequency, voltage magnitude and phase angle, simulating an on-line frequency relay. The inicial performance suggests that the proposed
The typical use of frequency estimation in power system is for protection schemes against loss of synchronism , underand-over frequency relaying and for power system stabilization . Generally, frequency relays are used in power system to protect the generating units. In addition to provide complete primary and back up protection. The performance of these relays, however, depends primarily on the accuracy of the frequency and voltage magnitude measurements. Some solutions have been suggested utilizing signal processing for the estimation problem. The applications can be categorized based on their time demand, that is: critical real time application, such as the mentioned relay protection; online data monitoring in control room and off-line data analysis from computer recordings. The classification is useful since the different time demands imply in restrictions on what type of frequency estimator and filter technique that can be used. In general, the available frequency estimation techniques use digitized samples of the system voltage. Considering the power system voltage waveforms as purely sinusoidal, the time between two zero crossings is an indication of system frequency. However, in reality, the measured signals are available in a distorted form and this can be a problem for the frequency estimation. Discrete Fourier transform, Least Error Square, Kalman filtering, orthogonal finite-impulse-response (FIR) filtering, and iterative approaches - are some of the important techniques in this area. Soft computing techniques, such as Artificial Neural Network (ANN)  and Genetic Algorithms (GAs)  are also utilized for power frequency measurement. This research presents a fast and efficient method based on GAs for measuring the frequency deviation as well as the voltage magnitude and phase angle of a sinusoid wave. In this approach, the problem is formulated as an optimization problem. The goal is to minimize the estimation error utilizing Gas, which have the advantage of having immunity against noise disturbance. However, it should be pointed out that one of the main objectives of this study is to investigate if the proposed GA approach for FPGA (Field-Programmable Gate Array) is able to accurately estimate the frequency, voltage magnitude and phase angle of a system. The performance of the proposed estimator is judged though numerous examples and the results obtained show that the technique estimates the waveform parameters with high degree of accuracy.