A new approach based on a linear Multi-Layer Perceptron for identifying on-line harmonics

A new approach based on a linear Multi Layer Perceptron (MLP) is introduced for harmonics identification. This neural approach uses linear neurons and inputs composed of synthetic harmonic terms in order to fit Fourier series of periodic signals. The amplitudes of the fundamental and high-order harmonics are deduced from a combination of the weights. The effectiveness of the approach is evaluated and compared to an Adaline-based method. Results show that the linear MLP is able to identify in real-time the amplitudes of harmonic terms from measured signals under noisy conditions. The approach can therefore be inserted in compensation strategies to ensure power quality in electrical grids.