Fault Identification of Power Grid Based on Wide-Area Differential Current and K-Means Clustering - Energy and Power Engineering

EPE > Vol.9 No.4, April 2017

Fault Identification of Power Grid Based on Wide-Area Differential Current and K-Means Clustering ()

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Download as PDF (Size: 330KB) PP. 19-29

DOI: 10.4236/epe.2017.94B003 2,535 Downloads 3,238 Views

Author(s)

Hao Wu^1,2, Qunzhan Li¹

Affiliation(s)

¹Electrical Engineering Institute, Southwest Jiaotong University, Chengdu, China.
²Automation and Electronic Information Engineering, Sichuan University of Science & Engineering, Zigong, China.

ABSTRACT

A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associated domain is defined, and the relationship of positive sequence current fault component for the association domain boundaries is sought, then the conception of positive sequence fault component differential current for node IED association domains is introduced. The information of the positive sequence fault component differential current gathered by node IEDs is selected as the object of K-means clustering. The node IEDs of fault associated domains can be classified into one category, and the node IEDs of non-fault associated domains are classified into another category. With the fault area minimum principle, the group of node IEDs about fault associated domains can be obtained. The overlap of fault associated domains for different nodes is the fault area. A large number of simulations show that the algorithm proposed can identify fault domains with high accuracy and no influence by the operating mode of the system and topological changes.

KEYWORDS

Positive Sequence Fault Component Differential Current, K-Means Clustering, Fault Association Domain, The Node IED, Fault Domain Identification

Share and Cite:

Wu, H. and Li, Q. (2017) Fault Identification of Power Grid Based on Wide-Area Differential Current and K-Means Clustering. Energy and Power Engineering, 9, 19-29. doi: 10.4236/epe.2017.94B003.

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