An Adaptive Denoising Algorithm for Online Condition Monitoring of High-Voltage Power Equipment
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Authors
Hussain, Ghulam
Ahmed, Zeeshan
Shafiq, Muhammad
Lehtonen, Matti
Rashid, Zeeshan
Zaher, Ashraf
Issue Date
2020-10-21
Type
Journal Article
Peer-Reviewed
Peer-Reviewed
Language
Keywords
Alternative Title
Abstract
Partial Discharge (PD) diagnostic is an effective tool for condition monitoring of the high
voltage equipment that provides an updated status of the dielectric insulation of the
components. Reliability of the diagnostics depends on the quality of the PD measurement
techniques and the processing of the measured PD data. The online measured data suffer from
various inaccuracies caused by external noise from various sources such as power electronic
equipment, radio broadband signals and wireless communication, etc. Therefore, extraction of
useful data from the on-site measurements is still a challenge. This article presents a discrete
wavelet transform (DWT) based adaptive de-noising algorithm and evaluates its performance.
Various decisive steps in applying DWT based de-noising on any signal, including selection
of mother wavelet, number of levels in multiresolution decomposition and criteria for
reconstruction of the de-noised signals are taken by the proposed algorithm and vary from one
signal to another without a human intervention. Hence, the proposed technique is adaptive.
The proposed solution can enhance the accuracy of the PD diagnostic for HV power
components.
Description
Citation
Hussain, A., Ahmed, Z., Shafiq, M., Zaher, A., Rashid, Z., & Lehtonen, M. (2020). An adaptive denoising algorithm for online condition monitoring of high-voltage power equipment. Electric Power Components and Systems, 48(9-10), 1036-1048. https://doi.org/10.1080/15325008.2020.1825554
Publisher
Taylor and Francis- Electric Power Components and Systems