Abstract
| Language | English |
|---|---|
| Pages | 1909 - 1916 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Instrumentation and Measurement |
| Volume | 62 |
| Issue number | 7 |
| Early online date | 11 Mar 2013 |
| DOIs | |
| Publication status | Published - 1 Jul 2013 |
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Keywords
- compositional modelling
- spectral characteristics
- partial discharge
- pulse
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Compositional modelling of partial discharge pulse spectral characteristics. / Baker, Peter; Stephen, Bruce; Judd, Martin.
In: IEEE Transactions on Instrumentation and Measurement, Vol. 62, No. 7, 01.07.2013, p. 1909 - 1916.Research output: Contribution to journal › Article
TY - JOUR
T1 - Compositional modelling of partial discharge pulse spectral characteristics
AU - Baker, Peter
AU - Stephen, Bruce
AU - Judd, Martin
PY - 2013/7/1
Y1 - 2013/7/1
N2 - Partial discharge (PD) monitoring is an established method for insulation health monitoring in high voltage plant. A number of different approaches to PD defect diagnosis have been developed to extract defect-specific information from PD pulse data in both the time and frequency domains. Frequency based PD pulse analysis has previously been demonstrated to offer a low-power approach to PD defect identification, where a mixture of passive and active analog electronics can be used to generate diagnostic features in a low-power device suited to wireless sensor network operation. This paper examines approaches to implementing diagnostic methods for frequency-based PD pulse diagnosis targeted at compositional frequency spectrum features in a computationally efficient manner. Dirichlet and Gaussian distributions are used to demonstrate the complex probabilistic form of fault class decision surfaces, which motivates the proposed application of the log ratio transform to frequency composition data. The results demonstrate that PD defects can be differentiated using these frequency-based methods and that employing the log ratio transform to the compositional frequency content data yields increases in classification accuracy without necessarily resorting to more complex classifiers.
AB - Partial discharge (PD) monitoring is an established method for insulation health monitoring in high voltage plant. A number of different approaches to PD defect diagnosis have been developed to extract defect-specific information from PD pulse data in both the time and frequency domains. Frequency based PD pulse analysis has previously been demonstrated to offer a low-power approach to PD defect identification, where a mixture of passive and active analog electronics can be used to generate diagnostic features in a low-power device suited to wireless sensor network operation. This paper examines approaches to implementing diagnostic methods for frequency-based PD pulse diagnosis targeted at compositional frequency spectrum features in a computationally efficient manner. Dirichlet and Gaussian distributions are used to demonstrate the complex probabilistic form of fault class decision surfaces, which motivates the proposed application of the log ratio transform to frequency composition data. The results demonstrate that PD defects can be differentiated using these frequency-based methods and that employing the log ratio transform to the compositional frequency content data yields increases in classification accuracy without necessarily resorting to more complex classifiers.
KW - compositional modelling
KW - spectral characteristics
KW - partial discharge
KW - pulse
UR - http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4407674
U2 - 10.1109/TIM.2013.2247711
DO - 10.1109/TIM.2013.2247711
M3 - Article
VL - 62
SP - 1909
EP - 1916
JO - IEEE Transactions on Instrumentation and Measurement
T2 - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
SN - 0018-9456
IS - 7
ER -