Hopfield neural network-based estimation of harmonic currents in power systems

A novel approach for adaptive estimation of instantaneous harmonic currents in the power system is proposed in this paper. The signal processing technique based on Hopfield neural network (HNN) optimum theory is applied for the detection of harmonic components generated by nonlinear current loads. Instead of training the network, the basic principle of neural network is applied to determine the magnitude and phase of the fundamental and each of the harmonic current components adaptively. The correct solution is obtained in real time. The computer simulation results show that the method has the characteristics of real time, high precision and adaptive tracing to load currents.