AN ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM STATIC VAR REGULATOR
El-Sadek, M., EI-Saady, G., Abo-El-Saud, M. (1997). AN ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM STATIC VAR REGULATOR. EKB Journal Management System, 7(ASAT Conf. 13-15 May 1997), 1-13. doi: 10.21608/asat.1997.25439
M. Z. El-Sadek; G. EI-Saady; M. Abo-El-Saud. "AN ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM STATIC VAR REGULATOR". EKB Journal Management System, 7, ASAT Conf. 13-15 May 1997, 1997, 1-13. doi: 10.21608/asat.1997.25439
El-Sadek, M., EI-Saady, G., Abo-El-Saud, M. (1997). 'AN ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM STATIC VAR REGULATOR', EKB Journal Management System, 7(ASAT Conf. 13-15 May 1997), pp. 1-13. doi: 10.21608/asat.1997.25439
El-Sadek, M., EI-Saady, G., Abo-El-Saud, M. AN ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM STATIC VAR REGULATOR. EKB Journal Management System, 1997; 7(ASAT Conf. 13-15 May 1997): 1-13. doi: 10.21608/asat.1997.25439

AN ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM STATIC VAR REGULATOR

International Conference on Aerospace Sciences and Aviation Technology
Article 47, Volume 7, ASAT Conf. 13-15 May 1997, May 1997, Page 1-13  XML PDF (1.98 MB)
Document Type: Original Article
DOI: 10.21608/asat.1997.25439
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Authors
M. Z. El-Sadek1; G. EI-Saady1; M. Abo-El-Saud2
1Electrical Engineering Department,Assiut University.
2Assiut Air-Port, Assiut, Egypt.
Abstract
An Artificial Neural Network (ANN) based power system static VAR controller is developed . The Static VAR Compensator (SVC) ( Thyristor Controlled -Fixed Capacitor (TCRFC) type) is used for voltage regulation and enhancing power system stability . The ANN is trained off-line using the Variable Structure control system data at different operating conditions and various external disturbances . Moreover, the trained ANN parameters are tuned and updated on-line using the synchronous machine speed deviation state as ANN output error . A sample digital simulation results of the power system speed deviation state responses are obtained when reference terminal voltage , speed state , and input torque disturbances take place . The digital simulation results prove the effectiveness and robustness of the present Adaptive Neural Network in terms of high performance power system and fast damping response of the power system electromechanical mode oscillations.
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