Feature Selection for Generator Excitation Neurocontroller Development Using Filter Technique
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 too. 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 filter technique is employed to select independent factors for ANN training.
Keywords: neural network, mlp, feature selection, regression analysis, generator excitation
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ABOUT THE AUTHORS
Abdul Ghani Abro
Abdul Ghani Abro received his B.E (in Electrical Engineering) degree from the MUET Jamshoro, Pakistan in 2005, the M.Engg degree from the NED University Karachi – Pakistan, in 2008 and currently pursuing PhD studies at Universiti Sains Malaysia. His research interests include computational intelligence & power system modeling and control.
Junita Mohamad Saleh
Junita Mohamad-Saleh received her B.Sc (in Computer Engineering) degree from the Case Western Reserve University, USA in 1994, the M.Sc. degree from the University of Sheffield, UK in 1996 and the Ph.D. degree from the University of Leeds, UK in 2002. She is currently an Associate Professor in the School of Electrical & Electronic Engineering, Universiti Sains Malaysia. Her research interests include computational intelligence, tomographic imaging and soft computing
Abdul Ghani Abro
Abdul Ghani Abro received his B.E (in Electrical Engineering) degree from the MUET Jamshoro, Pakistan in 2005, the M.Engg degree from the NED University Karachi – Pakistan, in 2008 and currently pursuing PhD studies at Universiti Sains Malaysia. His research interests include computational intelligence & power system modeling and control.
Junita Mohamad Saleh
Junita Mohamad-Saleh received her B.Sc (in Computer Engineering) degree from the Case Western Reserve University, USA in 1994, the M.Sc. degree from the University of Sheffield, UK in 1996 and the Ph.D. degree from the University of Leeds, UK in 2002. She is currently an Associate Professor in the School of Electrical & Electronic Engineering, Universiti Sains Malaysia. Her research interests include computational intelligence, tomographic imaging and soft computing
