Power system stability enhancement by single network adaptive critic stabilizers

Authors:
Gurunath Gurrala;Radhakant Padhi;Indraneel Sen
Affiliations:
Department of Electrical Engineering, Indian Institute of Science, Bangalore, India;Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India;Department of Electrical Engineering, Indian Institute of Science, Bangalore, India
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 6
Cited 0

Neural network design

Neural network design
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Applied Optimal Control and Estimation
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A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems

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Nonlinear decentralized control of large-scale power systems

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Comparison of heuristic dynamic programming and dual heuristic programming adaptive critics for neurocontrol of a turbogenerator

IEEE Transactions on Neural Networks

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Abstract

This paper proposes a Single Network Adaptive Critic (SNAC) based Power System Stabilizer (PSS) for enhancing the small-signal stability of power systems over a wide range of operating conditions. 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 stabilizer, which is relatively easier to synthesize, consistently outperformed stabilizers based on conventional lead-lag and linear quadratic regulator designs.