Title :
ANFIS approach for TCSC-based controller design for power system stability improvement
Author :
Khuntia, Swasti R. ; Panda, Sidhartha
Author_Institution :
Dept. of Electr. & Electron. Eng., Nat. Inst. of Sci. & Technol., Berhampur, India
Abstract :
In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on the Artificial Neural Network (ANN) is applied to design a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the power system stability. The design objective is to improve both rotor angle stability and system voltage profile. The proposed ANFIS controller combines the advantages of fuzzy controller and quick response and adaptability nature of ANN. The ANFIS structures were trained using the generated database by fuzzy controllers of TCSC. The results prove that the proposed TCSC-based ANFIS controller is found to be robust to fault location and change in operating conditions. Further, the results obtained are compared with the conventional lead-lag controllers for TCSC.
Keywords :
compensation; control system synthesis; fault location; flexible AC transmission systems; fuzzy control; neurocontrollers; power system stability; rotors; thyristors; ANFIS; ANN; TCSC; adaptive neuro-fuzzy inference system; artificial neural network; controller design; fault location; fuzzy controller; lead-lag controllers; power system stability; rotor angle stability; system voltage profile; thyristor controlled series compensator; Generators; Lead; Loading; Power capacitors; Thyristors; ANN; Adaptive Neuro-Fuzzy Inference System (ANFIS); Flexible AC Transmission System (FACTS); Fuzzy Logic Controller (FLC); Power System Stability; Single Machine Infinite Bus (SMIB); TCSC;
Conference_Titel :
Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference on
Conference_Location :
Ramanathapuram
Print_ISBN :
978-1-4244-7769-2
DOI :
10.1109/ICCCCT.2010.5670543