TY - JFULL AU - Rachid.Dehini and Abdesselam.Bassou and Brahim.Ferdi PY - 2009/10/ TI - Artificial Neural Networks Application to Improve Shunt Active Power Filter T2 - International Journal of Electrical and Computer Engineering SP - 1776 EP - 1784 VL - 3 SN - 1307-6892 UR - https://publications.waset.org/pdf/5898 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 33, 2009 N2 - Active Power Filters (APFs) are today the most widely used systems to eliminate harmonics compensate power factor and correct unbalanced problems 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 strategies for extracting the three-phase reference currents for active power filters and DC link voltage control method. The ANNs learning capabilities to adaptively choose the power system parameters for both to compute the reference currents and to recharge the capacitor value requested by VDC voltage in order to ensure suitable transit of powers to supply the inverter. To investigate the performance of this identification method, the study has been accomplished using simulation with the MATLAB Simulink Power System Toolbox. The simulation study results of the new (SAPF) identification technique compared to other similar methods are found quite satisfactory by assuring good filtering characteristics and high system stability. ER -