Abstract
Partial discharge (PD) may cause the insulation deterioration in power equipment and impact the reliability. Therefore, the PD detection with pattern recognition is an important tool in insulation diagnosis of High Voltage (HV) equipment. This paper presents the application of Possibilistic fuzzy c-means (PFCM) clustering approach to recognize PD patterns of HV equipment. The PD patterns are measured by using a commercial PD detector. A set of features used as operators for each PD pattern is extracted through statistical schemes. The significant features of PD patterns are extracted by using the nonlinear principal component analysis (NLPCA) method. The proposed PFCM classifier has an advantage of high robustness and efficacy to ambiguous patterns and is useful in recognizing the PD patterns of the HV equipment. To verify the effectiveness of the proposed method, the classifier has been verified on various test samples. The test results show that the proposed approach may achieve quite satisfactory recognition of PD patterns and the number of features in the feature vector will influence the accuracy of pattern recognition.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
