Reduction of time domain insulation response measurement duration for fast and effective diagnosis of power transformer

Mishra, D.; Pradhan, A. K.; Baral, Arijit; Chakravorti, S.

doi:10.1109/tdei.2018.007322

Cited by 21 publications

(18 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is understood that the magnitude of polarisation current remains unaffected even if it is measured for a short duration as long as the charging voltage (U 0 ) remains same. Therefore, the charge difference profile Q diff m2 t (obtained using i polar 0 ≤ t ≤ t 2 and i depolar red 0 ≤ t ≤ t 2 ) will have higher magnitude compared to Q diff m1 t for 0 < t<t 2 .Authors of the present paper have reported a neural network-based forecasting technique [14] for reducing polarisation current measurement time. Although applicable of polarisation current, this technique [14] is incapable of identifying profile of i depolar (t); 0 < t<10,000 using only i depolar red 0 < t ≤ t 2 .…”

Section: Developed Methodsmentioning

confidence: 91%

“…Therefore, the charge difference profile Q diff m2 t (obtained using i polar 0 ≤ t ≤ t 2 and i depolar red 0 ≤ t ≤ t 2 ) will have higher magnitude compared to Q diff m1 t for 0 < t<t 2 .Authors of the present paper have reported a neural network-based forecasting technique [14] for reducing polarisation current measurement time. Although applicable of polarisation current, this technique [14] is incapable of identifying profile of i depolar (t); 0 < t<10,000 using only i depolar red 0 < t ≤ t 2 . Consequently, technique mentioned in [14] finds limited application in determination of Q diff m1 t in the present work.…”

Section: Developed Methodsmentioning

confidence: 91%

“…It can be understood from above discussion that if the coefficients of (10) are identified properly, σ r can be evaluated using (12)- (14). From σ r , paper conductivity can be calculated using (5) and (6).…”

Section: Developed Methodsmentioning

confidence: 99%

“…Although applicable of polarisation current, this technique [14] is incapable of identifying profile of i depolar (t); 0 < t<10,000 using only i depolar red 0 < t ≤ t 2 . Consequently, technique mentioned in [14] finds limited application in determination of Q diff m1 t in the present work. Fig.…”

Section: Developed Methodsmentioning

confidence: 99%

“…This time can be significantly reduced using the proposed method. Using (14) along with (5) can essentially reduce σ paper computation time from 20,000 s (10,000 s each for polarisation and depolarisation current measurement) to only 2 × 800 s. Fig. 6 shows the profile of Q diff (t) obtained in the case of Trafo1 when t 2 is varied from 700 to 800 s. The red colour line in Fig.…”

Section: Developed Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Estimation of paper conductivity from short duration polarisation–depolarisation current for diagnosis of power transformer

Mishra

Haque

Baral

et al. 2019

IET Science, Measurement & Technology

View full text Add to dashboard Cite

Section: Developed Methodsmentioning

confidence: 91%

Section: Developed Methodsmentioning

confidence: 91%

Section: Developed Methodsmentioning

confidence: 99%

Section: Developed Methodsmentioning

confidence: 99%

Section: Developed Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Estimation of paper conductivity from short duration polarisation–depolarisation current for diagnosis of power transformer

Mishra

Haque

Baral

et al. 2019

IET Science, Measurement & Technology

View full text Add to dashboard Cite

Residual LSTM-based short duration forecasting of polarization current for effective assessment of transformers insulation

Vatsa,

Hati,

Kumar

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The empirical application of polarization and depolarization current (PDC) measurement of transformers facilitates the extraction of critical insulation-sensitive parameters. This technique, rooted in time-domain dielectric response analysis, forms the bedrock for parameterization and insulation modeling. However, the inherently time-consuming nature of polarization current measurements renders them susceptible to data corruption. This article explores deep-learning-based short-duration techniques for forecasting polarization current to address this limitation. By incorporating spatial shortcuts, the residual long short-term memory (LSTM) network facilitates the seamless propagation of spatial and temporal gradients. Furthermore, the relative forecasting assessment of the proposed residual LSTM model’s performance is made against traditional LSTM, attention LSTM, gated recurrent units (GRU), and convolutional neural network (CNN) models. Thus, optimal model selection strategies are evaluated based on their capability to capture extended dependencies and short-term information present in the data. In addition, the Monte Carlo dropout prediction is employed to estimate uncertainty in polarization current forecasts. The findings demonstrate that the proposed residual LSTM network model for polarization current forecasting yields the lowest error metrics and maintains prediction consistency over the testing duration. Thus, the proposed approach significantly reduces PDC measurement time, providing an effective means to develop proactive maintenance strategies for evaluating the insulation condition of transformers.

show abstract

Estimation of de‐trapped charge for diagnosis of transformer insulation using short‐duration polarisation current employing detrended fluctuation analysis

et al. 2020

View full text Add to dashboard Cite

Researchers have shown that the value of charge carriers, de‐trapped from the oil–paper interface of power transformer insulation, is useful in carrying out the diagnosis. However, the evaluation of the de‐trapped charge requires the analysis of polarisation–depolarisation currents. Being an off‐line time‐consuming process, the measurement and analysis of polarisation and depolarisation current (PDC) data are not practically advantageous. The study presents a detrended fluctuation analysis‐based technique to estimate the magnitude of normalised de‐trapped charge using the polarisation current measured for a short duration. Using the proposed technique, the requirement of measuring the complete PDC data, for diagnosis purposes, can be eliminated. Further, the technique also eliminates the requirement of depolarisation current which in turn facilitates a reduction in equipment shutdown time. The applicability of the proposed technique is tested on the data obtained from several real‐life power transformers.

show abstract

Reduction of time domain insulation response measurement duration for fast and effective diagnosis of power transformer

Cited by 21 publications

References 19 publications

Estimation of paper conductivity from short duration polarisation–depolarisation current for diagnosis of power transformer

Estimation of paper conductivity from short duration polarisation–depolarisation current for diagnosis of power transformer

Residual LSTM-based short duration forecasting of polarization current for effective assessment of transformers insulation

Estimation of de‐trapped charge for diagnosis of transformer insulation using short‐duration polarisation current employing detrended fluctuation analysis

Contact Info

Product

Resources

About