An Adaptive Neural Network Identifier for Effective Control of a Static Compensator Connected to a Power System
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Abstract
A novel method for nonlinear identification of a static compensator connected to a power system using continually online trained (COT) artificial neural networks (ANNs) is presented in this paper. The identifier is successfully trained online to track the dynamics of the power network without any need for offline data and can be used in designing an adaptive neurocontroller for a static compensator connected to such system.
This paper has been withdrawn.
