18,866 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
Power Quality Enhancement in Electricity Grids with Wind Energy Using Multicell Converters and Energy Storage
In recent years, the wind power industry is experiencing a rapid growth and more wind farms with larger size wind turbines are being connected to the power system. While this contributes to the overall security of electricity supply, large-scale deployment of wind energy into the grid also presents many technical challenges. Most of these challenges are one way or another, related to the variability and intermittent nature of wind and affect the power quality of the distribution grid. Power quality relates to factors that cause variations in the voltage level and frequency as well as distortion in the voltage and current waveforms due to wind variability which produces both harmonics and inter-harmonics. The main motivation behind work is to propose a new topology of the static AC/DC/AC multicell converter to improve the power quality in grid-connected wind energy conversion systems. Serial switching cells have the ability to achieve a high power with lower-size components and improve the voltage waveforms at the input and output of the converter by increasing the number of cells. Furthermore, a battery energy storage system is included and a power management strategy is designed to ensure the continuity of power supply and consequently the autonomy of the proposed system. The simulation results are presented for a 149.2 kW wind turbine induction generator system and the results obtained demonstrate the reduced harmonics, improved transient response, and reference tracking of the voltage output of the wind energy conversion system.Peer reviewedFinal Accepted Versio
Smart Meter Privacy: A Utility-Privacy Framework
End-user privacy in smart meter measurements is a well-known challenge in the
smart grid. The solutions offered thus far have been tied to specific
technologies such as batteries or assumptions on data usage. Existing solutions
have also not quantified the loss of benefit (utility) that results from any
such privacy-preserving approach. Using tools from information theory, a new
framework is presented that abstracts both the privacy and the utility
requirements of smart meter data. This leads to a novel privacy-utility
tradeoff problem with minimal assumptions that is tractable. Specifically for a
stationary Gaussian Markov model of the electricity load, it is shown that the
optimal utility-and-privacy preserving solution requires filtering out
frequency components that are low in power, and this approach appears to
encompass most of the proposed privacy approaches.Comment: Accepted for publication and presentation at the IEEE SmartGridComm.
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