Research on transformer fault diagnosis based on ISOMAP and IChOA‐LSSVM

Lu, Wanjie; Shi, Chun; Fu, Hua; Xu, Ya

doi:10.1049/elp2.12302

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…As an important electrical equipment in the power system, power transformers bear the heavy responsibility of voltage boost or voltage reduction [1]. Once a transformer failure occurs, it will seriously threaten the normal operation of the power system, so it is of great significance to improve the accuracy of transformer fault diagnosis for the stability and reliability of power system operation [2][3].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Power Transformers Based on Improved Golden Jackal Optimization Algorithm and Random Configuration Network

Shi

et al. 2023

IEEE Access

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The problem of the low accuracy of Dissolved Gas Analysis (DGA) in diagnosing transformer faults is addressed by proposing an Improved Golden Jackal Optimization (IGJO) based Stochastic Configuration Network (SCN) method. The method of transformer fault diagnosis based on IGJO optimized SCN is proposed. Firstly, Kernel Principal Component Analysis (KPCA) is used to reduce the dimensionality of the gas data and extract the effective feature quantities. Secondly, the L2 parametric penalty term is introduced into the SCN to improve the generalisation ability of SCN in practical applications. The elite backward learning and golden sine algorithms are incorporated into the golden jackal algorithm, and the IGJO performance is tested using 13 typical test functions, demonstrating that the IGJO has greater stability and merit-seeking capability. The penalty term coefficient C of the SCN is optimised using the IGJO to develop a transformer fault diagnosis model with an Improved Golden Jackal algorithm optimised Random Configuration Network (IGJO-SCN). Finally, the feature quantities extracted by KPCA are used as the input set of the model and the different transformer fault diagnosis models are simulated and validated. The results show that the IGJO-SCN has higher transformer fault diagnosis accuracy compared to other models.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Power Transformers Based on Improved Golden Jackal Optimization Algorithm and Random Configuration Network

Shi

et al. 2023

IEEE Access

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Research on Transformer Fault Diagnosis by WOA‐SVM Based on Feature Selection and Data Balancing

Ding,

Yu,

Liu

et al. 2024

IEEJ Transactions Elec Engng

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Oil‐immersed transformers as one of the most important equipment in the power system, the fault prediction of it in advance can effectively reduce the subsequent harm. Aiming at the selection of input features and data sample imbalance in the transformer fault diagnosis model, this paper adopts the recursive feature elimination (RFE) method combined with SMOTETomek comprehensive sampling method to optimize the above problems. First, RFE is used to traverse all the features and filter the optimal combination of them as input features, then SMOTETomek is used to perform balancing operation on the samples of the train set, and finally, whale optimization algorithm (WOA) is used to find the best hyperparameters for support vector machine (SVM), and the results are compared with the diagnostic models operated without processing and after single processing operation, respectively. After several sets of experiments, it is proved that the optimized comprehensive fault diagnosis model performs better on the test set than both the untreated and the singly processed models, which proves the effectiveness of the methodology used in this paper. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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Research on transformer fault diagnosis method based on ACGAN and CGWO-LSSVM

Guan,

Wu,

Yang

2024

Sci Rep

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This paper proposes a transformer fault diagnosis method based on ACGAN and CGWO-LSSVM to address the problem of misjudgment and low diagnostic accuracy caused by the small number and uneven distribution of some fault samples in transformer fault diagnosis. Firstly, generate adversarial networks through auxiliary classification conditions, The ACGAN method expands a small and imbalanced number of samples to obtain balanced and expanded data; Secondly, the non coding ratio method is used to construct the characteristics of dissolved gases in oil, and kernel principal component analysis is used, KPCA method for feature fusion; Finally, using the improved cubic gray wolf optimization algorithm, CGWO for least square support vector machines, optimize the parameters of the LSSVM model and construct a transformer fault diagnosis model. The results show that the proposed method has a low false alarm rate and a diagnostic accuracy of 97.66%, compared to IGOA-LSSVM the IChOA-LSSVM and PSO-LSSVM methods improved accuracy by 0.12, 1.76, and 2.58%, respectively. This method has been proven to solve the problems of misjudgment and low diagnostic accuracy caused by small sample sizes and uneven distribution. It is suitable for multi classification fault diagnosis of transformer imbalanced datasets and is superior to other methods.

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Research on transformer fault diagnosis based on ISOMAP and IChOA‐LSSVM

Cited by 4 publications

References 33 publications

Fault Diagnosis Method for Power Transformers Based on Improved Golden Jackal Optimization Algorithm and Random Configuration Network

Fault Diagnosis Method for Power Transformers Based on Improved Golden Jackal Optimization Algorithm and Random Configuration Network

Research on Transformer Fault Diagnosis by WOA‐SVM Based on Feature Selection and Data Balancing

Research on transformer fault diagnosis method based on ACGAN and CGWO-LSSVM

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