Fast estimation of voltage and current phasors in power networks using an adaptive neural network

A new algorithm for the estimation of parameters of voltage or current waveform of power networks contaminated by noise is proposed. The problem of estimation is formulated by using an adaptive neural network consisting of linear adaptive neurons called adaline. The learning parameters of the adaline are adjusted to force the error between the actual and desired outputs to satisfy a stable difference error equation, rather than to minimize an error function. Illustrative computer simulation results confirm the validity and accurate performance of the proposed method. Laboratory test results are also presented in this paper to support the effectiveness of the proposed approach in tracking the waveforms in real-time.