Detrapped Charge-Affected Depolarization-Current Estimation Using Short-Duration Dielectric Response for Diagnosis of Transformer Insulation

Banerjee, Chandra Madhab; Baral, Arijit; Chakravorti, S.

doi:10.1109/tim.2020.2984405

Cited by 15 publications

(5 citation statements)

References 21 publications

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“…According to the basic principle of PDC test, if the model parameters can be identified only by polarization current spectral lines, the test time can be halved, and the power outage time of the field test can be greatly reduced. The research results of reference [14] showed that the dielectric response analysis and parameter calculation results using only polarization current and using polarization/depolarization current at the same time are consistent, which verifies the rationality and effectiveness of polarization current as the original data.…”

Section: Introductionmentioning

confidence: 59%

Parameters Identification for Extended Debye Model of XLPE Cables Based on Sparsity-Promoting Dynamic Mode Decomposition Method

Wang,

Chen,

Yin

et al. 2023

Energy Engineering

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To identify the parameters of the extended Debye model of XLPE cables, and therefore evaluate the insulation performance of the samples, the sparsity-promoting dynamic mode decomposition (SPDMD) method was introduced, as well the basics and processes of its application were explained. The amplitude vector based on polarization current was first calculated. Based on the non-zero elements of the vector, the number of branches and parameters including the coefficients and time constants of each branch of the extended Debye model were derived. Further research on parameter identification of XLPE cables at different aging stages based on the SPDMD method was carried out to verify the practicability of the method. Compared with the traditional differential method, the simulation and experiment indicated that the SPDMD method can effectively avoid problems such as the relaxation peak being unobvious, and possessing more accuracy during the parameter identification. And due to the polarization current being less affected by the measurement noise than the depolarization current, the SPDMD identification results based on the polarization current spectral line proved to be better at reflecting the response characteristics of the dielectric. In addition, the time domain polarization current test results can be converted into the frequency domain, and then used to obtain the dielectric loss factor spectrum of the insulation. The integral of the dielectric loss factor on a frequency domain can effectively evaluate the insulation condition of the XLPE cable.

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Section: Introductionmentioning

confidence: 59%

Parameters Identification for Extended Debye Model of XLPE Cables Based on Sparsity-Promoting Dynamic Mode Decomposition Method

Wang,

Chen,

Yin

et al. 2023

Energy Engineering

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“…Fig. 4 shows the typical profile of polarization and depolarization current (PDC) [8], [13]. Keithley 6517B Electrometer is used for field test to get data from real-life transformer.…”

Section: Assessment Of Insulation Condition Using Pdc Anaysismentioning

confidence: 99%

“…Other Parameters, such as the Dielectric Absorption Ratio (DAR), which measures the insulation resistance at 60 seconds to 30 seconds, and the Polarization Index (PI), which measures the insulation resistance at 600 seconds to 60 seconds, are also used to determine the state of the insulation. These parameters can be obtained directly from the measured PDC data [8]. Therefore, if the polarization current is affected by the residual dipole energy or any other field issues, the above described performance parameters wrong information regarding the insulation condition.…”

Section: Assessment Of Insulation Condition Using Pdc Anaysismentioning

confidence: 99%

“…The accuracy of this method depends on CDM parameters resulted from relaxation current. Thus, it can be said that the reliability of information from the CDM is mainly depends on the measured PDC data [8]. Sometimes during field measurement, it is noticed that equipment unable to record the proper polarization current even under the application of dc voltage [9].…”

Section: Introductionmentioning

confidence: 99%

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Importance of Depolarization Current in the Diagnosis of Oil-Paper Insulation of Power Transformer

2023

Self Cite

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Recently, Polarization Depolarization current (PDC) measurement is widely accepted time domain spectroscopy-based method for assessing the insulation condition. Various performance parameters like Dissipation factor (%tanδ), Paper Moisture (%pm), Dielectric Adsorption Ratio (DAR), Polarization index (PI) etc. can be estimated by analyzing the PDC data. During field measurement various factors influences the recorded PDC data. As per existing literature, presence of low frequency noise, effect of temperature variation and influence of residual charge are common during field measurement. These factors significantly affect recorded polarization current and hence estimated performance parameters. Hence, analysis using recorded polarization current data may provide misleading information regarding insulation condition. Under such practical situation where polarization current is affected by above mentioned factors that generally observed during field measurement, depolarization current should be used for analysis of insulation condition. The depolarization current does not influence by such external factors. The present work shows the importance of depolarization current where polarization current is influenced by external low frequency noise and residual charge. The analysis firstly applied on sample prepared in the laboratory and then on data collected from real life in-situ transformers. The results obtained from the analysis shows that the data obtained from depolarization current is more reliable.

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“…The literature emphasizes the importance of time domain analysis techniques, such as PDC analysis concerning transformer oil-paper insulation, serving as a pivotal aspect of effective insulation assessment 7 . Diverse traditional insulation modeling techniques encompassing the conventional Debye mode (CDM) and the modified Debye model (MDM) provide a unique characterization of transformer insulation systems 8 , 9 . Furthermore, advanced techniques such as the model with time-varying parameters (MTVP) and the modified Maxwell model (MMM) provide a foundation for sophisticated methodologies to comprehend and evaluate insulation performance 10 .…”

Section: Introductionmentioning

confidence: 99%

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

Vatsa,

Hati,

Kumar

et al. 2024

Sci Rep

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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.

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Detrapped Charge-Affected Depolarization-Current Estimation Using Short-Duration Dielectric Response for Diagnosis of Transformer Insulation

Cited by 15 publications

References 21 publications

Parameters Identification for Extended Debye Model of XLPE Cables Based on Sparsity-Promoting Dynamic Mode Decomposition Method

Parameters Identification for Extended Debye Model of XLPE Cables Based on Sparsity-Promoting Dynamic Mode Decomposition Method

Importance of Depolarization Current in the Diagnosis of Oil-Paper Insulation of Power Transformer

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

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