Fault diagnosis of transformer based on modified grey wolf optimization algorithm and support vector machine

Huang, Xinyi; Huang, Xiaoli; Wang, Binrong; Xie, Zhenyu

doi:10.1002/tee.23069

Cited by 31 publications

(12 citation statements)

References 13 publications

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“…In the section, the diagnostic results of five methods will compared with the proposed method. These methods are IGWO-SVM [8], IGWO-PNN [16], HGWO-LSSVM [27], BA-SVM [29], and IKH-SVM [30].…”

Section: B Examplementioning

confidence: 99%

A Power Transformer Fault Diagnosis Method-Based Hybrid Improved Seagull Optimization Algorithm and Support Vector Machine

Sun

Zhang

et al. 2022

IEEE Access

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The method of Support Vector Machine (SVM) based on Dissolved Gas Analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings, such as the fuzzy boundaries of DGA data, and SVM parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on Kernel Principal Component Analysis (KPCA) and a hybrid improved Seagull Optimization Algorithm to optimize the SVM (TISOA-SVM). Firstly, KPCA is used to extract features from DGA feature quantities. In addition, TISOA is further proposed to optimize the SVM parameters to build the optimal diagnosis model based on SVM. For the SOA, three improvement methods are proposed. An improved tent map is used to replace the original population initialization to improve population diversity. In addition, the nonlinear inertia weight and random double helix formula are proposed to improve the optimization accuracy and efficiency of the SOA. Then, benchmarking functions are used to test the optimization performance of TISOA and six algorithms, and the results show that TISOA has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on KPCA and TISOA-SVM is obtained, and it is noteworthy that three examples are tested to verify the diagnostic performance of the proposed method. These results show that the proposed method has higher diagnostic accuracy, shorter diagnosis time, stronger significance and validity than other methods. Therefore, a research idea is provided for solving practical engineering problems in the field of fault diagnosis.INDEX TERMS Power transformer, fault diagnosis, kernel principal component analysis, support vector machine, hybrid improved seagull optimization algorithm.

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Section: B Examplementioning

confidence: 99%

A Power Transformer Fault Diagnosis Method-Based Hybrid Improved Seagull Optimization Algorithm and Support Vector Machine

Sun

Zhang

et al. 2022

IEEE Access

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“…Huang et al used the Differential Evolution (DE) to improve the Gray Wolf Optimizer (GWO), but they ignored the defects of DE and did not greatly improve the diagnosis results [8]. Yu et al used de and modified Sparrow Search Algorithm to optimize SVM, but it did not study the defects of DE, and the diagnosis performance of this model were not significantly improved [9].…”

Section: Introductionmentioning

confidence: 99%

A Transformer Fault Diagnosis Based on Improved Squirrel Search Algorithm and Support Vector Machine

Sun

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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An improved Squirrel Search Algorithm (ISSA) is proposed to overcome the shortcoming of Support Vector Machine (SVM) parameters is difficult to select. Firstly, an adaptive probability formula is used to balance the exploration and foraging ability of SSA, so as to improve the optimization performance of SSA. In order to test the optimization performance of ISSA, two test functions are used. And results show that ISSA has better optimization performance than SSA. Finally, based on ISSA-SVM and SVM, and 3-fold cross-Validation is used, the fault diagnosis of DGA data is carried out. The diagnostic results show that ISSA-SVM has the highest diagnosis performance.

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“…Zhong et al [15] established a diagnosis model based on convolutional neural network transmission learning and SVM and verified the effectiveness of the model through an example. For the accuracy of transformer fault diagnosis, Huang et al [16] proposed a diagnosis method based on an improved gray wolf algorithm and SVM. e differential evolution mechanism was introduced into the gray wolf optimization algorithm to improve its performance, and then the SVM optimized by the improved gray wolf algorithm was used for fault diagnosis of the transformer.…”

Section: Introductionmentioning

confidence: 99%

A New Support Vector Regression Model for Equipment Health Diagnosis with Small Sample Data Missing and Its Application

Liu

Mei

et al. 2021

Shock and Vibration

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Actually, it is difficult to obtain a large number of sample data due to equipment failure, and small sample data may also be missing. This paper proposes a novel small sample data missing filling method based on support vector regression (SVR) and genetic algorithm (GA) to improve equipment health diagnosis effect. First, the genetic algorithm is used to optimize support vector regression, and a new method GA-SVR can be proposed. The GA-SVR model is trained by using other data of the variable to which the missing data belongs, and the single-variable prediction method can be obtained. The correlation analysis is used to reconstruct the training set, and the GA-SVR is trained by using the data of the variables related to the missing data to obtain the multivariate prediction method. Then, the dynamic weight is presented to combine the single-variable prediction method with the multiple-variable prediction method based on certain principles, and the missing data are filled with the combined prediction methods. The filled data are used as input of GA-SVM to diagnose equipment failure. Finally, a case study is given to verify the applicability and effectiveness of the proposed method.

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Fault diagnosis of transformer based on modified grey wolf optimization algorithm and support vector machine

Cited by 31 publications

References 13 publications

A Power Transformer Fault Diagnosis Method-Based Hybrid Improved Seagull Optimization Algorithm and Support Vector Machine

A Power Transformer Fault Diagnosis Method-Based Hybrid Improved Seagull Optimization Algorithm and Support Vector Machine

A Transformer Fault Diagnosis Based on Improved Squirrel Search Algorithm and Support Vector Machine

A New Support Vector Regression Model for Equipment Health Diagnosis with Small Sample Data Missing and Its Application

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