1. Univ logo Fault Diagnosis for Power Transmission Line using Statistical Methods Yuanjun Guo Prof. Kang Li Queen’s University, Belfast UKACC PhD Presentation Showcase

2. Univ logo UKACC PhD Presentation Showcase Slide 2 Background Huge data

3. Univ logo UKACC PhD Presentation Showcase Slide 3 Problems & Motivation  Problems  Curse of Dimensionality  multivariate and correlated data  Classification  various types of faults in transmission lines  Motivation  Dimension reduction  Balance the real time implementation and the accuracy

4. Univ logo UKACC PhD Presentation Showcase Slide 4 Research methodology  Principal Component Analysis  Support Vector Machine  PLS, ICA, PCR etc.

5. Univ logo UKACC PhD Presentation Showcase Slide 5 Current status

6. Univ logo UKACC PhD Presentation Showcase Slide 6 Conclusion  Statistical approaches are capable of reduce the by capturing the between the from the data.  Statistical approaches are capable of reduce the data dimensionality by capturing the relationship between the recorded variables from the data.  Provide for the violate fault points.  Provide confidential limit charts for the violate fault points.  Extract the of the faulty signal under different faulty situations.  Extract the features of the faulty signal under different faulty situations.  SVMs uses the these faults correctly.  SVMs uses the features as input to classify these faults correctly.

7. Univ logo UKACC PhD Presentation Showcase Slide 7 Future work  Develop extensions to identify the nonlinear relations of the process variables;  Develop nonlinear and dynamic extensions to identify the nonlinear relations of the process variables;  for SVMs to achieve better classification results.  Optimize or select parameters for SVMs to achieve better classification results.  Research the application in power lines.  Research the application in power transmission lines.

8. Univ logo UKACC PhD Presentation Showcase Slide 8