Harmonic estimation in a power system using adaptive perceptrons

The paper presents an adaptive neural network approach for the estimation of harmonic components of a power system. The neural estimator is based on the use of an adaptive perceptron comprising a linear adaptive neuron called Adaline. The learning parameters in the proposed algorithm are adjusted to force the error between the actual and desired outputs to satisfy a stable difference error equation. The estimator tracks the Fourier coefficients of the signal data corrupted with noise and decaying DC components very accurately. Adaptive tracking of harmonic components of a power system can easily be done using this algorithm. Several numerical tests have been conducted for the adaptive estimation of harmonic components of power system signals mixed with noise and decaying DC components. Data from a laboratory test is used to validate the performance of this new approach.