Adaline-based approaches for time-varying frequency estimation in power systems

Two new neural approaches for on-line frequency estimation of a sinusoidal signal perturbed by harmonic distortions and random noise are presented in this paper. These approaches are based on an iterative formulation of the signal which is learned by Adaline neural networks. Adalines are very simple and efficient artificial neural networks, they can be easily implemented on a digital signal processor. The proposed approaches are therefore suitable for real-time implementations and their performance and robustness are evaluated by numerical simulations and experimentally under different severe operating conditions. The proposed neural estimators are favorably compared to the classical zero-crossing method, to an active notch filter method, and to a previous Adaline based-method. Furthermore, all these methods are also evaluated in terms of computational costs.