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- Bag, Gargi, et al.
(författare)
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Performance Evaluation of IEC 61850-90-5 over a latency optimized 3GPP LTE Network
- 2018
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Ingår i: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018. - : IEEE. - 9781538679548
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Konferensbidrag (refereegranskat)abstract
- This paper evaluates the performance of the IEC 61850-90-5 protocol stack transmitted over a latency optimized non-commercial 3GPP LTE network through experiments in a lab environment. IEC 61850-90-5 is designed for Phasor Measurement Unit (PMU) communication but can also be extended to other areas of control and protection in smart grids, using the routable GOOSE and routable sample value features. The test was carried out using a PC that emulated a PMU in sending periodic IEC61850-90-5 packets. Different IEC 61850-90-5 packet sizes and transmission rates were considered to determine the availability, throughput and latency of the LTE based network. The latency was minimized by applying a low latency scheduling mechanism and local breakout where user plane functionality of the core network is located at the edge of the Radio Access network. From the results it can be seen that an LTE network with latency optimized configuration performs well in terms of throughput and latency whereas a reference network without local breakout show significantly higher latency and jitter.
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| 2. |
- Balouji, Ebrahim, 1985, et al.
(författare)
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A deep learning approach to earth fault classification and source localization
- 2020
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Ingår i: IEEE PES Innovative Smart Grid Technologies Conference Europe. ; 2020-October, s. 635-639
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Konferensbidrag (refereegranskat)abstract
- A portion of electrical feeders in distribution grids are prone to faults, often resulting in different types of earth faults, power quality disturbances as well as damaged equipment and outages. While in developed countries the amount of such feeders can be relatively low, the quota reaches as high as 20% for many developing countries. Tackling this issue requires (i) understanding the current status of the grid and the faults that occur and (ii) identifying the origin of the fault for preventing similar future faults. This process is however costly and time consuming as it requires many hours of tedious manual work from engineers, operators or field experts. In an effort to tackle this issue, we present in this work a machine learning based framework for automatized fault type classification and faulty feeder identification. We provide an empirical evaluation of our proposed framework on a dataset of recordings from a real grid, showing encouraging results.
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