利用多因素降维的配电网区段定位完全解析模型

doi:10.19595/j.cnki.1000-6753.tces.180742

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Abstract The existing section location model of distribution network does not count the information missing and false alarms into fault variables, resulting in the inadequacy of fault tolerance of section location when multi-node information is distorted. In order to improve the fault tolerance of section location, a complete analytical model of section location in distribution network is constructed. In order to reduce the variable dimension of the complete analytic model, the fault contradiction hypothesis is used to reduce the variable dimension to three times the number of nodes. Secondly, the variable dimension of single fault location is further reduced by a hierarchical location model. Finally, when the diagnosis results, obtained by the method of model based diagnosis, act as the known variables of the first layer model, the variable dimension is reduced again. The case shows that compared with the models that do not take account of missing and false alarms, the fault-tolerant capability of the complete analytic model is greatly improved, and the warning message of missing and false alarm can be obtained synchronously. Compared with the complete analytical models that do not use multi-factor to reduce the dimension, the speed and accuracy of the proposed model are relatively high, which is more in line with the real-time requirements of section location.

Key words： Distribution network section location complete analytic model multi-factor dimen- sionality deduction

Received: 01 May 2018 Published: 29 July 2019

PACS:	TM72
	TM77

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	Articles by authors
	Wang Qiujie
	Jin Tao
	Shen Tao
	Zhu Shaojie
	Liu Suiyang

Cite this article:

Wang Qiujie,Jin Tao,Shen Tao等. A Complete Analytic Model of Section Location in Distribution Network Based on Multi-Factor Dimensionality Deduction[J]. Transactions of China Electrotechnical Society, 2019, 34(14): 3012-3024.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.180742 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I14/3012

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