Title

Online Identification of Generator Dynamics in a Multimachine Power System with a Spiking Neural Network

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.

Department(s)

Electrical and Computer Engineering

Sponsor(s)

National Science Foundation (U.S.)

Keywords and Phrases

Generator Dynamics; Gradient Descent Algorithms; Integrate-And-Fire Model; Inter-Spike Interval; Multi-Machine Power System; Natural Perturbations; New England; On-Line Identification; Power Networks; Simulation Result; Spiking Neural Networks; Spiking Neuron Networks; Terminal Voltages; Time Step; Training Conditions

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2009 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


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