Single network adaptive critic design for power system stabilisers

Gurrala, G and Sen, I and Padhi, Radhakant (2009) Single network adaptive critic design for power system stabilisers. In: IET Generation, Transmission & Distribution, 3 (9). pp. 850-858.

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Abstract

The recently developed single network adaptive critic (SNAC) design has been used in this study to design a power system stabiliser (PSS) for enhancing the small-signal stability of power systems over a wide range of operating conditions. PSS design is formulated as a discrete non-linear quadratic regulator problem. SNAC is then used to solve the resulting discrete-time optimal control problem. SNAC uses only a single critic neural network instead of the action-critic dual network architecture of typical adaptive critic designs. SNAC eliminates the iterative training loops between the action and critic networks and greatly simplifies the training procedure. The performance of the proposed PSS has been tested on a single machine infinite bus test system for various system and loading conditions. The proposed stabiliser, which is relatively easier to synthesise, consistently outperformed stabilisers based on conventional lead-lag and linear quadratic regulator designs.

Item Type:	Journal Article
Publication:	IET Generation, Transmission & Distribution
Publisher:	IEEE
Additional Information:	Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords:	Artificial neural network;excitation control
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering) Division of Electrical Sciences > Electrical Engineering
Date Deposited:	21 Dec 2009 06:57
Last Modified:	19 Sep 2010 05:46
URI:	http://eprints.iisc.ac.in/id/eprint/23483

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