Stochastic noise removal on partial discharge measurement for transformer insulation diagnosis
MetadataShow full item record
Measurement of partial discharge (PD) paves a way for transformer insulation diagnosis. However, noise always interferes with PD signals and can jeopardize the diagnostic reliability. Therefore, it is necessary to adopt signal processing techniques to remove noise from collected signals. Among various types of noise, stochastic noise is considerably difficult to remove due to its similarity with PD signals. This paper proposes an effective method, which adopts fractal dimension and entropy analyses to remove stochastic noise. To verify the proposed method, PD measurements have been performed on a number of experimental models and a substation transformer. Results prove that PD signals can be extracted while the noise can be eliminated from collected noise-corrupted signals by using the proposed method. A comparison with a wavelet transform-based noise removal method has also been made in the paper.
2014 IEEE Power & Energy Society General Meeting Proceedings
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Electrical and Electronic Engineering not elsewhere classified