Power quality assessment using an adaptive neural network

The paper presents an adaptive neutral network approach for the estimation of harmonic components of a power system and its power quality. The neural estimator is based on the use of an adaptive perceptron consisting of 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 performed using this algorithm. Several numerical tests have been conducted for the adaptive estimation of harmonic components, total harmonic distortion and power quality of power system signals mixed with noise and decaying DC components.