Feature Selection for Generator Excitation Neurocontroller Development Using Filter Technique

TL;DR

This paper explores using a filter technique for feature selection to improve the training and performance of an artificial neural network-based neurocontroller for generator excitation control in power systems.

Contribution

It introduces a filter-based feature selection method to optimize input features for neurocontroller development, enhancing control efficiency.

Findings

Selected features improve neurocontroller accuracy

Filter technique reduces training data complexity

Enhanced control response observed in simulations

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 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.