Online identification of generator dynamics in a multimachine power system with a spiking neural network

Authors:
Cameron Johnson;Ganesh K. Venayagamoorthy;Pinaki Mitra
Affiliations:
Real-Time Power and Intelligent Systems Laboratory at the Missouri University of Science and Technology, Rolla, MO;Real-Time Power and Intelligent Systems Laboratory at the Missouri University of Science and Technology, Rolla, MO;Real-Time Power and Intelligent Systems Laboratory at the Missouri University of Science and Technology, Rolla, MO
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 2
Cited 1

2007 Special Issue: Online design of an echo state network based wide area monitor for a multimachine power system

Neural Networks
Training Spiking Neuronal Networks With Applications in Engineering Tasks

IEEE Transactions on Neural Networks

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

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the application of a spiking neural network for online identification of generator dynamics in a multimachine power system. An integrate and fire model of a spiking neuron is used in this paper where the information is communicated through the interspike intervals. A network of spiking neurons is trained online based on a gradient descent algorithm. Speed and terminal voltage deviations of a generator in the IEEE 10-machine 39-bus New England power system are predicted one time step ahead by a spiking neural network. Two different training conditions are considered, namely, forced and natural perturbations. The simulation results show that a spiking neural network can successfully estimate the speed and terminal voltage deviations for both small and large perturbations applied to a power network.