2009 Special Issue: Comparison of a spiking neural network and an MLP for robust identification of generator dynamics in a multimachine power system

Authors:
Cameron Johnson;Ganesh Kumar Venayagamoorthy;Pinaki Mitra
Affiliations:
Real-Time Power and Intelligence Systems Laboratory, Missouri University of Science and Technology, Rolla, MO 65401, USA;Real-Time Power and Intelligence Systems Laboratory, Missouri University of Science and Technology, Rolla, MO 65401, USA;Real-Time Power and Intelligence Systems Laboratory, Missouri University of Science and Technology, Rolla, MO 65401, USA
Venue:
Neural Networks
Year:
2009

Citing 2
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Abstract

The application of a spiking neural network (SNN) and a multi-layer perceptron (MLP) for online identification of generator dynamics in a multimachine power system are compared in this paper. An integrate-and-fire model of an SNN which communicates information via the inter-spike interval is applied. The neural network identifiers are used to predict the speed and terminal voltage deviations one time-step ahead of generators in a multimachine power system. The SNN is developed in two steps: (i) neuron centers determined by offline k-means clustering and (ii) output weights obtained by online training. The sensitivity of the SNN to the neuron centers determined in the first step is evaluated on generators of different ratings and parameters. Performances of the SNN and MLP are compared to evaluate robustness on the identification of generator dynamics under small and large disturbances, and to illustrate that SNNs are capable of learning nonlinear dynamics of complex systems.