Effect of charge accumulated at oil–paper interface on parameters considered for power transformer insulation diagnosis

Mishra, D.; Haque, Nasirul; Baral, Arijit; Chakravorti, S.

doi:10.1049/iet-smt.2017.0354

Cited by 26 publications

(25 citation statements)

References 26 publications

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“…Furthermore, (11) shows that i condn is equal to (b-2|a|t fnl ). Discussion presented above suggests that i condn can be easily identified using (12), (13) and Q diff m1 0 ≤ t ≤ 10000 . Therefore, minimisation of time required for i condn estimation can be addressed by minimising span of PDC data 0 ≤ t ≤ t 2 which is used for identification of Q diff m1 0 ≤ t ≤ 10000 .…”

Section: Developed Methodsmentioning

confidence: 97%

“…In PDC measurement conducted on real-life transformers, it is often encountered the profile polarisation current does not completely match with the profile of depolarisation current, even when adjusted for conduction current. This is because of two reasons, the non-linearity present in oil-paper insulation and the extraction of interfacial charge during depolarisation process [12,13]. The proposed method is based on analysis of difference in charge quantity that is introduced/removed during PDC measurement [11].…”

Section: Developed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

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Section: Developed Methodsmentioning

confidence: 97%

Section: Developed Methodsmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

“…The ensemble members are selected in light of 2 rules, the accuracy and the diversity. The accuracy is evaluated by the mean squared errors (MSE), the mean absolute percentage errors (MAPE) and the correlation coefficient (CC), which are shown as formula (12) ∼ (14).…”

Section: Selecting the Ensemble Membersmentioning

confidence: 99%

“…where R(x, c i ) is the correlation coefficient between the geometric center of the training set S tr i and x. The correlation coefficient is shown as formula (14), and the geometric center of the training set S tr i is defined as formula (20).…”

Section: Integrating the Ensemble Membersmentioning

confidence: 99%

“…The box substation in wind farm may suffer greater than the conventional power transformer, due to the uncertainty of the wind power [9]- [12]. Both the rule based condition assessment and the machine learning based condition assessment cannot meet the requirements of''few people on duty'' operation mode, which is promoted by the wind farms in over the world [13], [14]. For these reasons, an on-line condition assessment of box substation in wind farm based on hypothesis testing is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

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An Online Condition Assessment of Box Substation in Wind Farm Based on Hypothesis Testing

et al. 2020

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With the rapid development of wind generation, the box substation is widely applied in the wind farm. However the box substation is more prone to break down than the conventional power transformer, due to the uncertainty of wind power. It is difficult for the existing transformer condition assessment to fulfill the requirement of ''few people on duty'' operation model, which is promoted by both the domestic and foreign wind farms. To improve the safety and reliability of the wind generation, an on-line condition assessment of the box substation in wind farm based on hypothesis testing is proposed. Firstly, an ensemble learning based on least squares support vector regression (EL-LSSVR) is proposed. Secondly, EL-LSSVR is applied to the historical data of the normal box substation, to establish a normal hot spot temperature model. Then the normal hot spot temperature model learns from the real-time data of the evaluated box substation on-line, to establish a real-time hot spot temperature model. Thirdly, the hypothesis testing is employed to analyze the significance between the real-time hot spot temperature and the normal hot spot temperature, which are generated by the real-time hot spot temperature model and the normal hot spot temperature model respectively. The condition assessment of the box substation is implemented according to the significance. Finally, the proposed method is validated by the condition assessment experiment of the box substation, which is installed at a wind farm in South China.

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Health status diagnosis of distribution transformers based on big data mining

Wang

Chen

et al. 2019

Trans Emerging Tel Tech

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In the fault detection technology of distribution transformer, the traditional artificial intelligence algorithm has been unable to achieve its efficient analysis and processing, but also unable to achieve the elimination of misdiagnosis caused by improper interval segmentation in the process of fault diagnosis of distribution transformer. In a word, the problem that always exists in transformer fault diagnosis technology is the problem of discretization of fault data. In order to solve this problem, this paper creatively proposes a health condition diagnosis method of distribution transformer based on large data. This method innovatively proposes that the dissolved gas analysis value of distribution transformer is the conditional attribute, and the fault type is the decision attribute, and the fault decision table is established. The continuous attribute data in the decision table are discretized by using the optimization behavior of large data sets. Subsequently, the discretized decision table is simplified by using the big data theory, and the decision table of fault diagnosis rules is established, which greatly simplifies the difficulty of attribute simplification of decision table and makes diagnosis more convenient. Finally, an example shows that the proposed method can effectively discretize and reduce samples. Compared with traditional methods, it improves the accuracy of fault diagnosis.

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Effect of charge accumulated at oil–paper interface on parameters considered for power transformer insulation diagnosis

Cited by 26 publications

References 26 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

An Online Condition Assessment of Box Substation in Wind Farm Based on Hypothesis Testing

Health status diagnosis of distribution transformers based on big data mining

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