Assessment of deterioration in epoxy/mica machine insulation

Kim, Y.J.; Nelson, J. K.

doi:10.1109/14.256478

Cited by 50 publications

(6 citation statements)

References 13 publications

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“…This is the classic result for P D occurring in voids, i.e. bounded by a solid dielectric [6,7,9]. Since asphaltic-mica insulation usually deteriorates by delamination of the groundwall insulation, the on-line PD pattern is consistent with off-line tests on stator coils and insulation models.…”

Section: Pulse Phase Analysis Resultssupporting

confidence: 69%

Unusual PD pulse phase distributions in operating rotating machines

Stone¹,

Goodeve²,

Sedding

et al. 1995

IEEE Trans. Dielect. Electr. Insul.

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The position of partial discharges (PD) with respect to power frequency phase position has long been known to provide useful information on the nature of deterioration of electrical insulation. In the past, virtually all such data has been from P D tests performed in the laboratory on static models or equipment. Improvements in the past 15 years in noise elimination technology have made it possible t o monitor the P D activity (number, magnitude and phase position) in the HV stator windings of operating motors and generators. Over a two year period, the PD activity was recorded on more than 100 motors, hydrogenerators and turbine generators, and the pulses analyzed with respect to the ac phase position, with a resolution of 3 . 6 ' . In the majority of stator windings, the P D pulses occurred in the expected portion of the ac phase, i.e. between 0 and 90' as well as between 180 and 270'. However a significant number of instances were recorded where the pulses appeared t o be shifted -30'. This is probably due t o the phase-

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Section: Pulse Phase Analysis Resultssupporting

confidence: 69%

Unusual PD pulse phase distributions in operating rotating machines

Stone¹,

Goodeve²,

Sedding

et al. 1995

IEEE Trans. Dielect. Electr. Insul.

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show abstract

“…In this study, several statistical parameters including skewness(Sk), kurtosis(Ku), cross correlation factor (Cc) and discharge asymmetry factor(Asy) for multiparameter diagnosis were selected. (9) where x i is the recorded value, p i is the probability of frequency of appearance for that value xi in time window i, μ is the mean value of x i ·p i , and is the variance. (11) where Q + and Q -are the sum of discharges of the pulse height in the positive and negative half cycles respectively.…”

Section: B Parameters Measurementmentioning

confidence: 99%

“…A method to quantitatively calculate the reliability of multi-parameter diagnosis is put forward. Moreover, as an example, referring to the different non-destructive diagnosis methods for generators [5][6][7][8][9], the reliability of multi-parameter diagnosis for the stator bar of generator (300 MW, 18 kV) is studied, and the optimized diagnosis algorithm is calculated.…”

Section: Introductionmentioning

confidence: 99%

The optimization of multi-parameter insulation diagnosis

Shen

et al. 2009

2009 IEEE Conference on Electrical Insulation and Dielectric Phenomena

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On the basis of statistical theory, a method to quantitatively determine the reliability of multi-parameter diagnosis and to optimize the algorithm of multi-parameter diagnosis is put forward. This method is based on the statistical law, and concerns correlation between the different parameters. Moreover, as an example, the stator bar of generator (300 MW, 18 kV) is studied. 30 samples were selected from different parts of the practical stator bar of generators, and the parameters of dielectric loss, partial discharge and remaining breakdown voltage (BDV, 50 Hz) of each sample were measured. This method is applied to the estimation BDV of generator bars and the optimized multi-parameter diagnosis algorithm is determined on the basis of actual data. Sk+, tan and C are selected as the optimal parameter group by this method. Comparing with experimental data, it shows that the quantity of parameters is not the more the better. To choose appropriate parameters for assessing insulation condition is important. In order to choose appropriate parameters, two following principles are suggested. 1) It is efficient to select the parameters which have significant correlation with estimation object, as well as to obviate those of limited correlation. 2) Correlation between parameters should be taken into account. Generally speaking, the degree of correlation between parameters is the lower the better.

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Section: Introductionmentioning

confidence: 99%

The reliability of multi-parameter insulation diagnosis

Shen

et al. 2010

IEEE Trans. Dielect. Electr. Insul.

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On the basis of statistical theory, a method to determine quantitatively the reliability of multi-parameter diagnosis and to optimize the algorithm of multi-parameter diagnosis is put forward. Moreover, as an example, this method is applied to the estimation of residual breakdown voltage of generator bars and the optimized multi-parameter diagnosis algorithm is determined on the basis of actual data. Comparing with experimental data, it shows that the result of multi-parameter prediction model is more accurate than that of single-parameter prediction model when choosing the appropriate parameters group, however the quantity of parameters is not the more the better. To choose appropriate parameters for assessing insulation condition is important.Index Terms -Multi parameter, insulation diagnosis, choice of parameters.

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Assessment of deterioration in epoxy/mica machine insulation

Cited by 50 publications

References 13 publications

Unusual PD pulse phase distributions in operating rotating machines

Unusual PD pulse phase distributions in operating rotating machines

The optimization of multi-parameter insulation diagnosis

The reliability of multi-parameter insulation diagnosis

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