Application of Improved PNN in Transformer Fault Diagnosis

Zhang, Xunyou; Zuo, Sun

doi:10.3390/pr11020474

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…With the rapid development of the power industry, the healthy and stable operation of power transformers as key equipment of the power system is crucial to the safe operation of the entire power system. Therefore, it is essential to proactively forecast the operational status of power transformers and promptly implement appropriate measures to address any anomalies [1]. During the long-term operation of the transformer, the internal insulating oil will deteriorate, causing a small amount of hydrocarbon gas to dissolve in the insulating oil [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil

Du,

Sheng,

Liu

et al. 2024

Processes

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The prediction of dissolved gas change trends in power transformer oil is very important for the diagnosis of transformer faults and ensuring its safe operation. Considering the time series and nonlinear features of the gas change trend, this paper proposes a novel robust extreme learning machine (ELM) model combining an improved data decomposition method for gas content forecasting. Firstly, the original data with nonlinear and sudden change properties will make the forecasting model unstable, and thus an improved variational modal decomposition (IPVMD) method is developed to decompose the original data to obtain the multiple modal dataset, in which the marine predators algorithm (MPA) optimization method is utilized to optimize the free parameters of the VMD. Second, the ELM as an efficient and easily implemented tool is used as the basic model for dissolved gas forecasting. However, the traditional ELM with mean square error (MSE) criterion is sensitive to the non-Gaussian measurement noise (or outliers). In addition, considering the nonlinear non-Gaussian properties of the dissolved gas, a new learning criterion, called extended maximum correntropy criterion (ExMCC), is defined by using an extended kernel function in the correntropy framework, and the ExMCC as a learning criterion is introduced into the ELM to develop a novel robust regression model (called ExMCC-ELM) to improve the ability of ELM to process mutational data. Third, a gas-in-oil prediction scheme is proposed by using the ExMCC-ELM performed on each modal obtained by the proposed IPVMD. Finally, we conducted several simulation studies on the measured data, and the results show that the proposed method has good predictive performance.

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Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil

Du,

Sheng,

Liu

et al. 2024

Processes

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“…And through the Mahalanobis distance (MD) to deal with the outlier samples in the dataset, to complete the cleaning of the DGA data, and then improve the quality and accuracy of the data. Meanwhile, the parameters of the random forest model have an important impact on its classification ability, and it is often difficult to adjust the model parameters in the actual diagnostic process, so the use of intelligent optimization algorithms combined with classification models has become an effective means [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Transformer fault diagnosis based on the improved QPSO and random forest

Liu,

Cai,

Yan

et al. 2024

Meas. Sci. Technol.

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Although dissolved gas analysis (DGA) is an effective method for transformer fault diagnosis, problems with the quality and accuracy of DGA characterization datasets often arise in practical industrial applications and face difficulties in adjusting the parameters of fault diagnosis models. To address the above problems, this paper proposes a fault diagnosis model (MD-IQPSO-RF) based on Mahalanobis distance (MD) data cleaning and improved quantum particle swarm (IQPSO) optimization of random forest (RF) parameters. Specifically, the abnormal samples of the DGA dataset are first processed by MD to improve the quality and accuracy of the dataset. Then, the RF parameters were optimized using the IQPSO algorithm to adjust the model parameters in order to improve the diagnostic performance of the RF. Finally, the optimal parameters of RF are output, and the training data are used to train the RF algorithm to construct the MD-IQPSO-RF transformer fault diagnosis model. The experimental results show that the model achieves an average accuracy of 93.631% for fault diagnosis, which is 6.92% higher than the unoptimized RF model. Comparison with other similar methods also achieved good results, which further validated the effectiveness of the fault diagnosis model.

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Progress of fiber Bragg grating sensors in state perception of electrical equipment

Sun,

2023

Measurement

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Application of Improved PNN in Transformer Fault Diagnosis

Cited by 8 publications

References 18 publications

A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil

A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil

Transformer fault diagnosis based on the improved QPSO and random forest

Progress of fiber Bragg grating sensors in state perception of electrical equipment

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