@article{(Open Science Index):https://publications.waset.org/pdf/5898,
	  title     = {Artificial Neural Networks Application to Improve Shunt Active Power Filter},
	  author    = {Rachid.Dehini and  Abdesselam.Bassou and  Brahim.Ferdi},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {3},
	  number    = {9},
	  year      = {2009},
	  pages     = {1777 - 1784},
	  ee        = {https://publications.waset.org/pdf/5898},
	  url   	= {https://publications.waset.org/vol/33},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 33, 2009},
	}