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Neural-network-based adaptive UPFC for improving transient stability performance of power system.
- Source :
-
IEEE transactions on neural networks [IEEE Trans Neural Netw] 2006 Mar; Vol. 17 (2), pp. 461-70. - Publication Year :
- 2006
-
Abstract
- This paper uses the recently proposed H(infinity)-learning method, for updating the parameter of the radial basis function neural network (RBFNN) used as a control scheme for the unified power flow controller (UPFC) to improve the transient stability performance of a multimachine power system. The RBFNN uses a single neuron architecture whose input is proportional to the difference in error and the updating of its parameters is carried via a proportional value of the error. Also, the coefficients of the difference of error, error, and auxiliary signal used for improving damping performance are depicted by a genetic algorithm. The performance of the newly designed controller is evaluated in a four-machine power system subjected to different types of disturbances. The newly designed single-neuron RBFNN-based UPFC exhibits better damping performance compared to the conventional PID as well as the extended Kalman filter (EKF) updating-based RBFNN scheme, making the unstable cases stable. Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation. Also, all the machines are being equipped with the conventional power system stabilizer (PSS) to study the coordinated effect of UPFC and PSS in the system.
Details
- Language :
- English
- ISSN :
- 1045-9227
- Volume :
- 17
- Issue :
- 2
- Database :
- MEDLINE
- Journal :
- IEEE transactions on neural networks
- Publication Type :
- Academic Journal
- Accession number :
- 16566472
- Full Text :
- https://doi.org/10.1109/tnn.2006.871706