DocumentCode :
3007889
Title :
A cooperative CMAC neural network for hydro-generating system with doubly fed induction generators
Author :
Hui, Li ; Li, Han
Volume :
1
fYear :
2005
fDate :
27-29 Sept. 2005
Firstpage :
764
Abstract :
This paper proposes an approach of cooperative cerebellar model articulation controller (CMAC) neural network that is based on the concept of combining CMAC and the adaptive linear neuron controller. The main purpose is to eliminate the excess self-learning and chattering phenomena of the traditional feedforward cooperative CMAC. In the proposed approach, the CMAC adopts the dynamic errors and desired output values of the controlled plants for the input vectors, it is parallel to the adaptive neuron controller which can play an important role in improving the robustness and adaptive property of CMAC. In order to verify the control quality of the proposed cooperative CMAC neural network, the digital simulation of the steady state regulation characteristics for the multivariable and nonlinear control of the hydro-generating system with doubly fed induction generators (DFIG) is studied. By comparison the traditional proportional-integral-differential (PID) controller, the simulation results have shown that the proposed scheme is of good adaptability and robustness under the generator parameter variation, the hydro-turbine model changed and external load disturbance.
Keywords :
adaptive control; asynchronous generators; cerebellar model arithmetic computers; cooperative systems; feedforward; hydroelectric generators; multivariable control systems; neurocontrollers; nonlinear control systems; power generation control; robust control; three-term control; PID controller; adaptive linear neuron controller; cerebellar model articulation controller; cooperative CMAC neural network; digital simulation; doubly fed induction generators; external load disturbance; feedforward cooperative CMAC; generator parameter variation; hydrogenerating system; hydroturbine model; multivariable control; nonlinear control; proportional-integral-differential controller; robustness improvement; steady state regulation characteristics; Adaptive control; Digital simulation; Error correction; Induction generators; Neural networks; Neurons; Nonlinear control systems; Programmable control; Robust control; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Machines and Systems, 2005. ICEMS 2005. Proceedings of the Eighth International Conference on
Print_ISBN :
7-5062-7407-8
Type :
conf
DOI :
10.1109/ICEMS.2005.202638
Filename :
1574871
Link To Document :
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