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Feature Selection for Generator Excitation Neurocontroller Development Using Filter Technique

Author(s): Abdul Ghani Abro | Junita Mohamad Saleh

Journal: International Journal of Computer Science Issues
ISSN 1694-0784

Volume: 8;
Issue: 5;
Start page: 108;
Date: 2011;
Original page

Keywords: neural network | mlp | feature selection | regression analysis | generator excitation | IJCSI

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.

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