Estimation of power system harmonics using hybrid RLS-Adaline and KF-Adaline algorithms

This paper presents combined RLS-Adaline (recursive least square and adaptive linear neural network) and KF-Adaline (Kalman filter Adaline) 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. Kalman filter and recursive least square algorithms carry out the weight updating in Adaline. The estimators´ track the signal corrupted with noise and decaying DC components very accurately. Adaptive tracking of harmonic components of a power system can easily be done using these algorithms. The proposed approaches are tested both for static and dynamic signal. Out of these two, the KF-Adaline approach of tracking the fundamental and harmonic components is better.