The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON POWER SYSTEMS
Transactions ID Number: 53-568
Full Name: Abdul Ghani Abro
Position: Ph.D. Candidate
Age: ON
Sex: Male
Address: School of Electrical & Electronics Engineering; Engineering Campus, Universiti Sains Malaysia; 14300 Nibong Tebal, Seberang Perai Selatan, Penang
Country: MALAYSIA
Tel: +604-134982798
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Fax:
E-mail address: aga10_eee097@student.usm.my
Other E-mails: ghaniabro@gmail.com
Title of the Paper: Features Selection for Generator Excitation Neurocontroller Development Using Filter Technique
Authors as they appear in the Paper: Abdul Ghani Abro, Junita Mohamad-Saleh
Email addresses of all the authors: aga10_eee097@student.usm.my , jms@eng.usm.my
Number of paper pages: 10
Abstract: Essentially, motive behind using control system is to generate suitable control signal for yielding desired response of a physical process. Control of synchronous generator has always remained very critical in power system operation and control. For certain well known reasons power generators are normally operated well below their steady state stability limit. This raises demand for efficient and fast controllers. Artificial intelligence has been reported to give revolutionary outcomes in the field of control engineering. Artificial Neural Network (ANN), a branch of artificial intelligence has been used for nonlinear and adaptive control, utilizing its inherent observability. The overall performance of neurocontroller is dependent upon input features. Selecting optimum features to train a neurocontroller optimally is very critical. Both quality and size of data are of equal importance for better performance. In this work statistical methods are employed to select i!
ndependent factors for ANN training.
Keywords: generator excitation, transient stability enhancement, neural network, MLP, regression analysis, filtering technique
EXTENSION of the file: .pdf
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How Did you learn about congress: Power System Stability, Neurocontrol and Feature Selection
IP ADDRESS: 202.170.51.235
