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Fault Diagnosis for Power Transmission Line using Statistical Methods

Yuanjun Guo

Prof. Kang Li

Queen’s University, Belfast

UKACC PhD Presentation Showcase

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Background

Huge data

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Problems & Motivation

ProblemsCurse of Dimensionality

multivariate and correlated data

Classification various types of faults in transmission lines

MotivationDimension reductionBalance the real time implementation

and the accuracy

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Research methodology

Principal Component Analysis

Support Vector Machine

PLS, ICA, PCR etc.

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Current status

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Conclusion

Statistical approaches are capable of reduce the data dimensionality by capturing the relationship between the recorded variables from the data.

Provide confidential limit charts for the violate fault points.

Extract the features of the faulty signal under different faulty situations.

SVMs uses the features as input to classify these faults correctly.

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Future work

Develop nonlinear and dynamic extensions to identify the nonlinear relations of the process variables;

Optimize or select parameters for SVMs to achieve better classification results.

Research the application in power transmission lines.

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