Title :
Neural network control of the unified power flow controller
Author :
Zebirate, Soraya ; Chaker, Abdelkader ; Feliachi, Ali
Author_Institution :
LAAS Lab., Algeria
Abstract :
This paper investigates an efficient and robust control method for the UPFC in order to improve the stability of the power system, thus providing the security for the increased power flow. With neural networks, uncertainty or unknown variations in plant parameters and structure can be dealt with more effectively and hence improving the robustness of the control system. On the other hand control theory allows transient and steady state characteristics of the closed loop system to be specified. The effectiveness of this control structure is demonstrated under different operating conditions of the UPFC system.
Keywords :
adaptive control; load flow control; neurocontrollers; power system stability; predictive control; robust control; adaptive hybrid control; closed loop system; control theory; neural internal model control; neural network control; power system stability; predictive control; robust control method; steady state characteristics; unified power flow controller; Control systems; Load flow; Neural networks; Power system control; Power system security; Power system stability; Power system transients; Power systems; Robust control; Robust stability;
Conference_Titel :
Power Engineering Society General Meeting, 2004. IEEE
Print_ISBN :
0-7803-8465-2
DOI :
10.1109/PES.2004.1372858
