Open AccessDeep learning‐based fault location of DC distribution networks
Author(s) -
Guomin Luo,
Yingjie Tan,
Changyuan Yao,
Yinglin Liu,
Jinghan He
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8902
Subject(s) - fault (geology) , computer science , artificial neural network , converters , deep learning , artificial intelligence , power (physics) , feature (linguistics) , topology (electrical circuits) , real time computing , engineering , electrical engineering , seismology , geology , linguistics , philosophy , physics , quantum mechanics , voltage
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.