Monopolar Grounding Fault Location Method of DC Distribution Network Based on Improved ReliefF and Weighted Random Forest
Yan Xu,
Ziqi Hu () and
Tianxiang Ma
Additional contact information
Yan Xu: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University (Baoding), Baoding 071003, China
Ziqi Hu: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University (Baoding), Baoding 071003, China
Tianxiang Ma: State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China
Energies, 2022, vol. 15, issue 19, 1-23
Abstract:
Compared with the pole-to-pole short circuit, the fault characteristics are not obvious when a monopolar grounding fault occurs in a DC distribution network, and it is difficult to locate the fault accurately. To solve this problem, this paper proposes a fault location method based on improved ReliefF and Weighted random forest (WRF). The 24 time and frequency-domain fault features of the postfault aerial mode current are calculated, and the most useful features are selected to form the optimal feature subset for input to the fault location estimator. In this paper, the ReliefF algorithm is utilized for automatic feature selection and obtaining the weights of features. In addition, the WRF algorithm is used to build the fault location estimator. Considering the fault location, fault resistance, noise and time window length, the Matlab/Simulink simulation platform is used to simulate the fault situation and compare it with other algorithms. The simulation results show that the average positioning error of the fault location method is less than 0.1%, which is not affected by the fault resistance and has strong robustness.
Keywords: DC distribution network; fault location; monopolar grounding fault; ReliefF; weighted random forest (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://www.mdpi.com/1996-1073/15/19/7261/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/19/7261/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:19:p:7261-:d:932535
Access Statistics for this article
Energies is currently edited by Ms. Agatha Cao
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().