基于多测点正序电压相关性与典型模式匹配寻优的电压暂降源定位

doi:10.19595/j.cnki.1000-6753.tces.161213

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Abstract A novel method of voltage sag source location based on the correlation of multipoint positive sequence voltage and the typical pattern matching optimization is proposed to make the location results independent of fault type and transition resistance. Firstly, it builds the fault pattern using the positive sequence voltage variation at measuring points and defines the pattern similarity based on the Euclidean distance measure and the Spearman rank correlation coefficient. Then it forms the set of typical patterns by setting up a preset fault at the middle of each branch and clusters it into several subgroups according to the similarity of the measuring sequence and typical patterns. It chooses the corresponding lines of the subgroup with the highest similarity to make up the primary fault line set, which can effectively narrow the searching space. Finally, an optimal estimation model is built to find the optimal matching fault pattern. The object function is to maximize the similarity, the primary fault line and fault distance are chosen as optimization variables. The model is solved by particle swarm optimization algorithm, fault line and fault position can be obtained. The numerical examples of IEEE 30 and IEEE 33 system show the feasibility and validity of the proposed method. The results show that the method can effectively eliminate the influence of fault type and transition resistance and has higher location accuracy than previous methods.

Key words： Voltage sag fault location pattern similarity matching optimization correlation

Received: 08 August 2016 Published: 14 September 2017

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TM727

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	Lin Yongyi
	Shao Zhenguo
	Zhang Yan
	Zhang Yi
	Wu Danyue

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Lin Yongyi,Shao Zhenguo,Zhang Yan等. Voltage Sag Source Location Based on the Correlation of Multipoint Positive Sequence Voltage and the Typical Pattern Matching Optimization[J]. Transactions of China Electrotechnical Society, 2017, 32(17): 35-46.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.161213 OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I17/35

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