scholarly journals Automatic PRPD Image Recognition of Multiple Simultaneous Partial Discharge Sources in On-Line Hydro-Generator Stator Bars

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 326
Author(s):  
Ramon C. F. Araújo ◽  
Rodrigo M. S. de Oliveira ◽  
Fabrício J. B. Barros
Keyword(s):  
Partial Discharge ◽  
Classification Problem ◽  
Input Pattern ◽  
Partial Discharges ◽  
Data Set ◽  
Stator Windings ◽  
Unknown Sample ◽  
On Line ◽  
A New Technique ◽  
Generator Stator

In this study, a methodology for automatic recognition of multiple simultaneous types of partial discharges (PDs) in hydro-generator stator windings was proposed. All the seven PD sources typical in rotating machines were considered, and up to three simultaneous sources could be identified. The functionality of identifying samples with no valid PDs was also incorporated using a new technique. The data set was composed of phase-resolved partial discharge (PRPD) patterns obtained from on-line measurements of hydro-generators. From an input PRPD, noise and interference were removed with an improved version of an image-based denoising algorithm previously proposed by the authors. Then, a novel image-based algorithm that separates partially superposed PD clouds was proposed, by decomposing the input pattern into two sub-PRPDs containing discharges of different natures. From the sub-PRPDs, one extracts features quantifying the PD distribution over amplitudes and the contour of PD clouds. Those features are fed as inputs to several artificial neural networks (ANNs), each of which solves a part of the classification problem and acts as a block of a larger system. Once trained, ANNs work collaboratively to identify an unknown sample. Good results were obtained, with overall accuracies ranging from 88% to 94.8% for all the considered PD sources.

scholarly journals Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3267
Author(s):  
Ramon C. F. Araújo ◽  
Rodrigo M. S. de Oliveira ◽  
Fernando S. Brasil ◽  
Fabrício J. B. Barros
Keyword(s):  
Image Denoising ◽  
Recognition Performance ◽  
Partial Discharge ◽  
Classification Performance ◽  
Partial Discharges ◽  
Denoising Method ◽  
Internal Parameters ◽  
On Line ◽  
Automatic Removal ◽  
The Impact

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected.

scholarly journals On-line Partial Discharge Measurement of Hydrogenerator Stator Windings using Acoustic Emission Detection Techniques

2004 ◽  
Vol 124 (2) ◽  
pp. 274-280 ◽  
Author(s):  
Tadamitsu Kaneko ◽  
Akito Takemura ◽  
Osamu Takenouchi ◽  
Youl-Moon Sung ◽  
Masahisa Otsubo ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Partial Discharge ◽  
Discharge Measurement ◽  
Detection Techniques ◽  
Stator Windings ◽  
On Line ◽  
Emission Detection

Generator Stator End Winding Resonance: Problems and Solutions

2013 ◽  
Author(s):  
Bill Moore ◽  
Clyde Maughan
Keyword(s):  
Visual Inspection ◽  
Fatigue Cracking ◽  
Vibration Monitoring ◽  
Stator Winding ◽  
Resonant Vibration ◽  
Primary Methods ◽  
Stator Windings ◽  
Problems And Solutions ◽  
On Line ◽  
Generator Stator

Stator windings that are in resonance will have high levels of vibration, if not properly damped or braced. Windings in resonance can suffer from early conductor strand fatigue cracking, arcing and failure during operation. Evidence of high vibration can sometimes be seen through visual inspection, with observance of dusting and greasing. There are two primary methods to anticipate and detect end winding resonant vibration — the bump test and on-line monitoring. Both are important and play a key role in identifying stator winding resonance problems, as well as implementing the appropriate solution. This paper will discuss the reasons that stator end winding resonance occurs. The technology, as well as the advantages and limitations of both the bump test and vibration monitoring, will be discussed. Solution approaches to end winding vibration are included, as well as one case history.

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