A novel synchronous fault identification strategy of electronic transformer based on synergy of historical data

Lin, Xin; Xing, Jian; Chen, Dunhui; Li, Zhengtian; Rong, Zirui; Liu, Yao; Liu, Shiming; Su, Shu; Tong, Ning; Jing, Rong

doi:10.1016/j.ijepes.2018.06.007

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…The results in Tab. 8 show that, compared with 10 groups of actual fault types known in advance, there are 9 groups with the same results and 1 group with different results. That is, the diagnostic accuracy rate of the BTS's fault diagnosis model based on VPRS-RBFNN is 90%, indicating that this model is feasible.…”

mentioning

confidence: 92%

“…Hu et al [6] mined the casing data of oil-immersed transformers and used Apriori algorithm and Tanimoto coefficient to evaluate the relationship between state parameters; based on Pearson correlation coefficient, the fault diagnosis matrix was constructed, which judges the fault diagnosis mode of the equipment. Feng et al [7] used the method of data fusion to establish the equipment diagnosis model of power transformer, which reduced the uncertainty of fault diagnosis; Lin et al [8] found the discrete model of topology structure and state of power system of transformer from a large number of historical data, and proved the synchronous fault principle of transformer. In order to overcome the limitations of DGA method, Abu-Siada and Hmood [9] introduced fuzzy logic to identify transformer faults more accurately.…”

Section: Introductionmentioning

confidence: 99%

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On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN

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mentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN

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Research on adaptive state prediction method for the metering error of capacitor voltage transformer

Zhu,

Binbin,

Jianyi

et al. 2023

Review of Scientific Instruments

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A capacitor voltage transformer (CVT) is widely used in high voltage power systems because of its good insulation performance. However, the structure of CVT is more complex and the stability of its metering error is poor, which easily causes the loss of power metering. The conventional evaluation method for the metering error of CVT is to compare and calibrate with a standard transformer under off-line condition in a fixed period. Because of the long evaluation period, it is impossible to accurately predict the state change of CVT metering error, which is of more significance practically. To solve this problem, this paper proposes an adaptive state prediction method: Analyze the measurement data of CVT using principal component analysis method under the constraint of electrical physical relationship, the metering of CVT is mapped to residual and score (CRS) statistic. For this way, the self-evaluation of CVT metering error in real-time is realized without a standard transformer to get the high frequency time series of error data. According to the measurement data of CVT in process, the CRS statistics are batch processed adaptively, and the prediction model of CRS statistics is established based on the time series analysis. Experiments show that the method can accurately predict the state change of CVT metering error, and the prediction error is better than 15%. It is helpful to promote the development of CVT metering error detection into on-demand detection.

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Prediction model of measurement errors in current transformers based on deep learning

Li,

Cui,

et al. 2024

Review of Scientific Instruments

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The long-term monitoring stability of electronic current transformers is crucial for accurately obtaining the current signal of the power grid. However, it is difficult to accurately distinguish between the fluctuation of non-stationary random signals on the primary side of the power grid and the gradual error of the transformers themselves. A current transformer error prediction model, CNN-MHA-BiLSTM, based on the golden jackal optimization (GJO) algorithm, which is used to obtain the optimal parameter values, bidirectional long short-term memory (BiLSTM) network, convolutional neural networks (CNNs), and multi-head attention (MHA), is proposed to address the difficulty of measuring error evaluation. This model can be used to determine the operation of transformers and can be widely applied to assist in determining the stability of transformer operation and early faults. First, CNN is used to mine the vertical detail features of error data at a certain moment, improving the speed of error prediction. Furthermore, a cascaded network with BiLSTM as the core is constructed to extract the horizontal historical features of the error data. The GJO algorithm is used to adjust the parameters of the BiLSTM model; optimize the hidden layer nodes, training frequency, and learning rate; and integrate MHA mechanism to promote the model to pay attention to the characteristic changes of the data in order to improve the accuracy of error prediction. Finally, this method is applied to the operation data of transformer in substations, and four time periods of data are selected to verify the model effectiveness of the current transformer dataset. The analysis results of single step and multi-step examples indicate that the proposed model has significant advantages in terms of accuracy and stability in error prediction.

show abstract

A novel synchronous fault identification strategy of electronic transformer based on synergy of historical data

Cited by 4 publications

References 31 publications

On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN

On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN

Research on adaptive state prediction method for the metering error of capacitor voltage transformer

Prediction model of measurement errors in current transformers based on deep learning

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