DocumentCode
2771516
Title
Adaptive NeuroFuzzy Legendre based damping control paradigm for SSSC
Author
Badar, Rabiah ; Khan, Laiq
Author_Institution
Dept. of Electr. Eng., COMSATS Inst. of Inf. Technol., Abbottabad, Pakistan
fYear
2012
fDate
4-7 Sept. 2012
Firstpage
1
Lastpage
6
Abstract
The controllable series injected voltage can be used to damp low frequency power and rotor angle oscillations. Conventional linear and NeuroFuzzy control schemes perform well only for a specific operating condition, or in the vicinity of the tuned operating point of highly nonlinear power system, due to their fixed parameters architecture. To improve the performance of the damping control, nonlinear behavior of power system must be incorporated via some nonlinear control scheme. This work presents an online adaptive nonlinear control paradigm by incorporating Legendre polynomial NNs in the consequent part of the conventional TSK structure. The proposed control scheme is successfully applied to damp local and inter-area modes of oscillations for different contingencies and operating conditions. The robustness of the proposed control scheme is validated using comparative analysis based on nonlinear time domain simulations and different performance indices.
Keywords
adaptive control; fuzzy control; fuzzy neural nets; linear systems; neurocontrollers; nonlinear control systems; polynomials; power system stability; static VAr compensators; time-domain analysis; Legendre polynomial NN; SSSC; TSK structure; adaptive neurofuzzy Legendre based damping control paradigm; damping control; interarea mode damping; linear control schemes; low frequency power oscillation damping; nonlinear control scheme; nonlinear power system; nonlinear time domain simulations; online adaptive nonlinear control paradigm; rotor angle oscillation damping; static synchronous series compensator; Control systems; Damping; Mathematical model; Oscillators; Polynomials; Power system stability; Voltage control; Adaptive control; FACTS; Legendre NN; MATLAB/SIMULINK; Multi-machine system; NeuroFuzzy; SMIB; SSSC; TSK;
fLanguage
English
Publisher
ieee
Conference_Titel
Universities Power Engineering Conference (UPEC), 2012 47th International
Conference_Location
London
Print_ISBN
978-1-4673-2854-8
Electronic_ISBN
978-1-4673-2855-5
Type
conf
DOI
10.1109/UPEC.2012.6398433
Filename
6398433
Link To Document