Through the looking glass: Seeing events in power systems dynamics
| Autor: | Pedro A. Cardoso, Vladimiro Miranda, Ricardo J. Bessa, I.C. Decker |
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| Rok vydání: | 2019 |
| Předmět: |
Pixel
business.industry Computer science 020209 energy Computer Science::Neural and Evolutionary Computation 020208 electrical & electronic engineering Phasor Graphics processing unit Energy Engineering and Power Technology 02 engineering and technology Perceptron Convolutional neural network Rendering (computer graphics) Units of measurement Deep belief network Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering Computer vision Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | International Journal of Electrical Power & Energy Systems. 106:411-419 |
| ISSN: | 0142-0615 |
| DOI: | 10.1016/j.ijepes.2018.10.024 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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