Electric Power ›› 2025, Vol. 58 ›› Issue (9): 68-78.DOI: 10.11930/j.issn.1004-9649.202502061

• Key Technologies for Enhancing the Grid Connection Safety Capability of New Energy and New Grid-Connected Entities • Previous Articles     Next Articles

Fault Location Method for Secondary System of Smart Substations Based on Network Flow Algorithm and Deep Neural Network

TAO Jun1(), ZHONG Ming1(), ZHANG Yi2(), LIU Feng1, WU Yuzhu1(), XIA Zhenxing3()   

  1. 1. Inner Mongolia Electric Power (Group) Co., Ltd., Inner Mongolia Electric Power Science Research Institute Branch, Hohhot 010020, China
    2. Inner Mongolia Electric Power (Group) Co., Ltd., Wuhai Ultra-high Voltage Power Supply Branch, Wuhai 016000, China
    3. Wuhan Kaimo Electric Co., Ltd., Wuhan 430223, China
  • Received:2025-02-26 Online:2025-09-26 Published:2025-09-28
  • Supported by:
    This work is supported by National Key Research and Development Program of China (No.2023YFB2405900), 2024 Science and Technology Project of Inner Mongolia Electric Power (Group) Co., Ltd. (No.2024-4-50).

Abstract:

The existing intelligent substation secondary system fault location method relies on specific types of fault feature quantities, lacking the ability toively process multiple fault types. It is unable to quickly modify the scheme when facing dynamic changes in the power network. To address this challenge, a fault location method based on the network algorithm and deep neural network (DNN) is proposed. A new fault type classification method is adopted, and the simple fault, pseudo-complex fault, and complex fault are re. A fault feature coding and matrix relationship model are constructed, and the network flow algorithm is introduced to solve the problem of fuzzy positioning of link fault and node fault in complex fault. The network flow algorithm is deeply integrated with the deep neural network model to achieve accurate positioning of intelligent substation secondary system faults. Through simulation example comparison, it is found that the method can not only improve the accuracy of complex fault recognition and shorten the fault location time, but also effectively cope with the dynamic changes of the power system, and improve the fault location.

Key words: smart substation, network flow algorithm, deep neural network, fault location