2,713 research outputs found
Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning
This paper presents a spatiotemporal unsupervised feature learning method for
cause identification of electromagnetic transient events (EMTE) in power grids.
The proposed method is formulated based on the availability of
time-synchronized high-frequency measurement, and using the convolutional
neural network (CNN) as the spatiotemporal feature representation along with
softmax function. Despite the existing threshold-based, or energy-based events
analysis methods, such as support vector machine (SVM), autoencoder, and
tapered multi-layer perception (t-MLP) neural network, the proposed feature
learning is carried out with respect to both time and space. The effectiveness
of the proposed feature learning and the subsequent cause identification is
validated through the EMTP simulation of different events such as line
energization, capacitor bank energization, lightning, fault, and high-impedance
fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the
WSCC 9-bus system.Comment: 9 pages, 7 figure
Encoding Robust Representation for Graph Generation
Generative networks have made it possible to generate meaningful signals such
as images and texts from simple noise. Recently, generative methods based on
GAN and VAE were developed for graphs and graph signals. However, the
mathematical properties of these methods are unclear, and training good
generative models is difficult. This work proposes a graph generation model
that uses a recent adaptation of Mallat's scattering transform to graphs. The
proposed model is naturally composed of an encoder and a decoder. The encoder
is a Gaussianized graph scattering transform, which is robust to signal and
graph manipulation. The decoder is a simple fully connected network that is
adapted to specific tasks, such as link prediction, signal generation on graphs
and full graph and signal generation. The training of our proposed system is
efficient since it is only applied to the decoder and the hardware requirements
are moderate. Numerical results demonstrate state-of-the-art performance of the
proposed system for both link prediction and graph and signal generation.Comment: 9 pages, 7 figures, 6 table
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