Artificial Neural Networks for Harmonic Estimation in Low-Voltage Power Systems

Harmonic estimation is the foundation of every active noise canceling method in low-voltage power systems. It generates and re-injects reference currents in phase opposition through an active power line conditioner. Active Power Filters (APFs) are today the most widely used systems to compensate harmonics in industrial power plants. We propose to improve the performances of conventional APFs by using artificial neural networks (ANNs) for harmonics estimation. This new method combines both the advantages of conventional APF to compute instantaneous real and imaginary powers and the learning capabilities of ANNs to adaptively choose the parameters of the power system. In fact, the separation of the powers is implemented with an Adaline neural network which uses a priori known frequencies as inputs. Furthermore, multilayer feedforward networks are used to approximate the instantaneous powers and to compute the reference currents. Simulation results show the reliability of the method and better performances than conventional APFs. I