300 research outputs found
Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks
This paper develops a novel graph convolutional network (GCN) framework for
fault location in power distribution networks. The proposed approach integrates
multiple measurements at different buses while taking system topology into
account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus
benchmark system. Simulation results show that the GCN model significantly
outperforms other widely-used machine learning schemes with very high fault
location accuracy. In addition, the proposed approach is robust to measurement
noise and data loss errors. Data visualization results of two competing neural
networks are presented to explore the mechanism of GCN's superior performance.
A data augmentation procedure is proposed to increase the robustness of the
model under various levels of noise and data loss errors. Further experiments
show that the model can adapt to topology changes of distribution networks and
perform well with a limited number of measured buses.Comment: Accepcted by IEEE Journal on Selected Areas in Communicatio
Convolutional Sequence to Sequence Non-intrusive Load Monitoring
A convolutional sequence to sequence non-intrusive load monitoring model is
proposed in this paper. Gated linear unit convolutional layers are used to
extract information from the sequences of aggregate electricity consumption.
Residual blocks are also introduced to refine the output of the neural network.
The partially overlapped output sequences of the network are averaged to
produce the final output of the model. We apply the proposed model to the REDD
dataset and compare it with the convolutional sequence to point model in the
literature. Results show that the proposed model is able to give satisfactory
disaggregation performance for appliances with varied characteristics.Comment: This paper is submitted to IET-The Journal of Engineerin
Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features
The detection and characterization of partial discharge (PD) are crucial for
the insulation diagnosis of overhead lines with covered conductors. With the
release of a large dataset containing thousands of naturally obtained
high-frequency voltage signals, data-driven analysis of fault-related PD
patterns on an unprecedented scale becomes viable. The high diversity of PD
patterns and background noise interferences motivates us to design an
innovative pulse shape characterization method based on clustering techniques,
which can dynamically identify a set of representative PD-related pulses.
Capitalizing on those pulses as referential patterns, we construct insightful
features and develop a novel machine learning model with a superior detection
performance for early-stage covered conductor faults. The presented model
outperforms the winning model in a Kaggle competition and provides the
state-of-the-art solution to detect real-time disturbances in the field.Comment: To be published in IEEE Transactions on Smart Gri
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