Title :
Radial basis function network based power system stabilizers for multimachine power systems
Author :
Abido, M.A. ; Abdel-Magid, Y.L.
Author_Institution :
Dept. of Electr. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
Abstract :
A radial basis function network (RBFN) based power system stabilizer (PSS) is presented in this paper to improve the dynamic stability of multimachine power systems. The proposed RBFN is trained over a wide range of operating conditions in order to re-tune the parameters of the PSS in real-time. Time domain simulations of a multimachine power system with different operating conditions subject to a three phase fault are studied and investigated. The performance of the proposed RBFN PSS is compared to that of conventional power system stabilizer (CPSS). The results show the good damping characteristics of the proposed RBFN PSS over a wide range of operating conditions
Keywords :
feedforward neural nets; neurocontrollers; power system stability; RBFN PSS; dynamic stability; multimachine power systems; radial basis function network based power system stabilizers; Damping; Power system control; Power system dynamics; Power system faults; Power system measurements; Power system modeling; Power system simulation; Power system stability; Power systems; Radial basis function networks;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.616093
