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Wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage

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3 Author(s)
Hasanien, H.M. ; Electr. Eng. Dept., King Saud Univ., Riyadh, Saudi Arabia ; Ali, S.Q. ; Muyeen, S.M.

This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) to enhance the transient stability of a grid-connected wind generator system. The control strategy of the SMES unit is developed based on cascaded control scheme of a voltage source converter and a two-quadrant DC-DC chopper using insulated gate bipolar transistors (IGBTs). The proposed controller is used to control the duty cycle of the DC-DC chopper. Detailed modeling and control strategies of the system are presented. The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of a conventional proportional-integral (PI)-controlled SMES. The validity of the proposed system is verified with the simulation results which are performed using the standard dynamic power system simulator PSCAD/EMTDC.

Published in:

Electrical Machines and Systems (ICEMS), 2012 15th International Conference on

Date of Conference:

21-24 Oct. 2012

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