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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 6271341, 16 pages
Research Article

Neural Adaptive Decentralized Coordinated Control with Fault-Tolerant Capability for DFIGs under Stochastic Disturbances

1School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China
2School of Economics and Management, Northeast Electric Power University, Jilin 132012, China
3State Key Laboratory of Alternate Electric Power System with Renewable Power Source, North China Electric Power University, Beijing 102206, China

Correspondence should be addressed to Hong Cao; nc.ude.upecn@hc

Received 9 March 2017; Accepted 25 July 2017; Published 10 October 2017

Academic Editor: Mohammad D. Aliyu

Copyright © 2017 Xiao-ming Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


At present, most methodologies proposed to control over double fed induction generators (DFIGs) are based on single machine model, where the interactions from network have been neglected. Considering this, this paper proposes a decentralized coordinated control of DFIG based on the neural interaction measurement observer. An artificial neural network is employed to approximate the nonlinear model of DFIG, and the approximation error due to neural approximation has been considered. A robust stabilization technique is also proposed to override the effect of approximation error. A controller and a controller are employed to achieve specified engineering purposes, respectively. Then, the controller design is formulated as a mixed optimization with constrains of regional pole placement and proportional plus integral (PI) structure, which can be solved easily by using linear matrix inequality (LMI) technology. The results of simulations are presented and discussed, which show the capabilities of DFIG with the proposed control strategy to fault-tolerant control of the maximum power point tracking (MPPT) under slight sensor faults, low voltage ride-through (LVRT), and its contribution to power system transient stability support.