期刊
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
卷 106, 期 -, 页码 411-419出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2018.10.024
关键词
Convolution neural networks; Deep belief networks; Image recognition; Multilayer perceptrons; GPU; Phasor measurement units; Power system dynamics
资金
- ERDF - European Regional Development Fund (COMPETE 2020 Programme)
- FCT - Fundacao para a Ciencia e a Tecnologia, Portugal [POCI-01-0145-FEDER-016731 INFUSE]
This paper presents a new method to identify classes of events, by processing phasor measurement units (PMU) frequency data through deep neural networks. Deep tapered Multi-layer Perceptrons of the half-autoencoder type, Deep Belief Networks and Convolutional Neural Networks (CNN) are compared, using real data from Brazil. A sound success is obtained by a transformation of time-domain signals, from dynamic events recorded, into 2D images; these images wee processed with a CNN, taking advantage of the strong dependency existing among neighboring pixels in images. The training, computing and processing was achieved with a GPU (Graphics Processing Unit), allowing speeding-up of the process up to 30 times and rendering the process suitable to increase the online situational awareness of network operators.
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