Fault Current Tracing and Identification via Machine Learning Considering Distributed Energy Resources in Distribution Networks

Wanghao Fei and Paul Moses
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Wanghao Fei: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
Paul Moses: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA

Energies, 2019, vol. 12, issue 22, 1-12

Abstract: The growth of intermittent distributed energy sources (DERs) in distribution grids is raising many new operational challenges for utilities. One major problem is the back feed power flows from DERs that complicate state estimation for practical problems, such as detection of lower level fault currents, that cause the poor accuracy of fault current identification for power system protection. Existing artificial intelligence (AI)-based methods, such as support vector machine (SVM), are unable to detect lower level faults especially from inverter-based DERs that offer limited fault currents. To solve this problem, a current tracing method (CTM) has been proposed to model the single distribution feeder as several independent parallel connected virtual lines that traces the detailed contribution of different current sources to the power line current. Moreover, for the first time, the enhanced current information is used as the expanded feature space of SVM to significantly improve fault current detection on the power line. The proposed method is shown to be sensitive to very low level fault currents which is validated through simulations.

Keywords: current tracing; fault current; distributed energy resources; network model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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