• ISSN 0258-2724
  • CN 51-1277/U
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Volume 55 Issue 3
Jun.  2020
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Article Contents
Journal of Southwest Jiaotong University  > 2020  >  55(3): 570-578.
GAO Fengyang, LI Zhaojun, YUAN Cheng, LI Xiaofeng, QI Xiaodong. Hierarchical Fault Location Method for Distribution Network with Special Load[J]. Journal of Southwest Jiaotong University, 2020, 55(3): 570-578. doi: 10.3969/j.issn.0258-2724.20190414
Citation: GAO Fengyang, LI Zhaojun, YUAN Cheng, LI Xiaofeng, QI Xiaodong. Hierarchical Fault Location Method for Distribution Network with Special Load[J]. Journal of Southwest Jiaotong University, 2020, 55(3): 570-578. doi: 10.3969/j.issn.0258-2724.20190414
shu

Hierarchical Fault Location Method for Distribution Network with Special Load

doi: 10.3969/j.issn.0258-2724.20190414
  • Received Date: 13 May 2019
  • Rev Recd Date: 12 Dec 2019
    • Available Online: 16 Jan 2020
  • Publish Date: 01 Jun 2020
    Abstract
  • Special load is connected to the distribution network, which enhances the interaction between the power supply, load and power grid, and imposes higher requirements for fault location. To deal with this, a hierarchical location method for fast locating fault points is proposed. Firstly, the incompleteness of the genetic algorithm based fault location model is analyzed for single-layer distribution network with special load. Secondly, based on the theory of the single-layer model, a regional positioning method and a segment method are proposed, which respectively improves quantum immune algorithm and implicit enumeration method. Finally, a comparative simulation is carried out between the proposed model and the single-layer model that is based on a single intelligent algorithm. Compared with the genetic algorithm, immune algorithm and improved single-layer positioning model constructed by quantum immune algorithm, the hierarchical positioning method can greatly simplify the complexity of the fault identification model, ensure the fault tolerance and stability of the positioning while improving the positioning efficiency. In addition, this method reduces the fault search dimension by 69%, takes less than 1s to locate the fault, and the fault identification rate is 100%.

     

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