Online Identification of Generator Dynamics in a Multimachine Power System with a Spiking Neural Network
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.
C. E. Johnson et al., "Online Identification of Generator Dynamics in a Multimachine Power System with a Spiking Neural Network," Proceedings of the International Joint Conference on Neural Networks (IEEE-IJCNN), Institute of Electrical and Electronics Engineers (IEEE), Jun 2009.
The definitive version is available at https://doi.org/10.1109/IJCNN.2009.5179057
Electrical and Computer Engineering
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
Article - Conference proceedings
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