RT Journal Article
A1 Luo Guomin
A1 Tan Yingjie
A1 Yao Changyuan
A1 Liu Yinglin
A1 He Jinghan

PB iet
T1 Deep learning-based fault location of DC distribution networks
JN The Journal of Engineering
VO 2019
IS 16
SP 3301
OP 3305
AB Compared with AC distribution networks, DC ones have a number of advantages. Intensive connections of distributed renewable energy can lead to large amount of power electronic converters and complex models. Underground cable is widely used in DC distribution networks. Accurate location of faults can help engineers find the fault points and shorten the time of maintenance. In DC distribution networks, where only a few measuring units are equipped and low sampling rates are adopted, there is limited data used for fault location. Also, for monopole grounding fault, the fault features are sometimes unobvious for recognition. Deep learning which provides feature hierarchy can learn experiences automatically and recognise raw data as human brain does. It reveals a high potential to solve location problems in DC distribution systems. This paper proposes a depth learning based fault location for DC distribution networks. First, a DC distribution network with radiant topology is modelled, and faults are added with different parameters to simulate various scenarios in practical projects. Then, a deep neural network is generated and trained with normalised fault currents. The parameters of network are discussed according to particular application. Finally, the location performance of deep neural network is tested.
K1 distributed renewable energy
K1 DC distribution network
K1 deep learning-based fault location
K1 deep neural network
K1 depth learning-based fault location
K1 AC distribution networks
K1 DC distribution systems
DO https://doi.org/10.1049/joe.2018.8902
UL https://digital-library.theiet.org/;jsessionid=4q35o2a55kw5f.x-iet-live-01content/journals/10.1049/joe.2018.8902
LA English
SN
YR 2019
OL EN