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In some digital application systems, power system frequency tracking is an important task. Accurate power frequency estimation is a necessity to check the state of health of power index, and a guarantee for accurate quantitative measurement of power parameters such as voltages, currents, active power and reactive power, in multi-function power meters under steady states. Many researches have been done in this area. Three criteria that a frequency tracking method should satisfy is given as follows (Akke,1997): 1. Fast speed of convergence 2. Accuracy of frequency estimation 3. Robustness to noise. He compares traditional modulation with new modulation. Traditional demodulation introduces a double frequency component that needs to be filtered away. For signals with low noise, the filter to reduce the double frequency component can often limit the speed of the frequency estimation algorithm. The purpose of this section is to show that the proposed method eliminates this problem. If other filters are the bottle-neck of the estimation algorithm, we will not capitalise on the benefits. Many well-proven techniques such as zero-crossing technique, level-crossing technique, least squares error technique, Newton method, Kalman filter, Fourier transform, and wavelet transform have been used for power harmonic frequency estimation in the fields of measurement, instrumentation, control and monitoring of power systems. Besides, a comprehensive analysis of discrete Fourier transform (DFT) error is given in some researches, including the cases of synchronous sampling and error rises when sampling frequency does not synchronize with signal frequency. A frequency tracking method based on linear estimation of phase (LEP) has been introduced. Also, a processing unit for symmetrical components and harmonic estimation based on an adaptive linear combiner has been proposed. This section presents the application of a complex adaptive linear neural network (CADALINE) in tracking the fundamental power system frequency. In this method, by using stationary-axes Park transformation in addition to producing a complex input measurement, the decaying DC offset is effectively eliminated. As the proposed method uses a first-order differentiator to estimate frequency changes, a Hamming filter is used to

A Novel Frequency Tracking Method Based on Complex Adaptive Linear Neural Network State Vector in Power Systems