Deep learning‐based fault location of DC distribution networks. Issue 16 (17th December 2018)

• Record Type: Journal Article
• Authors: Guomin, Luo; Yingjie, Tan; Changyuan, Yao; Yinglin, Liu; Jinghan, He
• Is Part Of: Journal of engineering. Volume 2019:Issue 16(2019)
• Page Start: 3301
• Page End: 3305
• Publication Date: 2018-12-17
• Subjects: distribution networks -- neural nets -- learning (artificial intelligence) -- distributed power generation -- power system stability -- power engineering computing -- fault currents -- fault diagnosis -- power distribution faults -- power system control -- power electronics -- underground cables -- fault location; distributed renewable energy -- DC distribution network -- DC distribution systems -- depth learning‐based fault location -- deep neural network -- deep learning‐based fault location -- AC distribution networks; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.8902
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : 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.