Transmission Line Fault Diagnosis Method Based on Improved Multiple SVM Model

Sun, Peichuan; Liu, Xuefei; Lin, Meng; Wang, Jie; Jiang, Tao; Wang, Yibo

doi:10.1109/access.2023.3333939

Cited by 5 publications

(1 citation statement)

References 29 publications

(36 reference statements)

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“…Ref. [13] proposed a transmission line fault diagnosis method based on an improved multiple SVM model that can overcome the limitations of small samples and generalization accuracy. Moreover, Refs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

Lin,

Wang,

et al. 2024

IEEE Access

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Power grid fault diagnosis methods based on analytical models possess advantages such as logical rigor, strong interpretability, and high applicability. However, the existing design of their objective functions is presented as a 0-1 integer nonlinear programming model, making it difficult to approximate the optimal solution. To address this issue, this paper proposes an improvement to the expression of the second backup protection expectation involved in the diagnostic model. Thus, the order of the objective function is successfully reduced, transforming the original 0-1 integer nonlinear programming model into a constrained 0-1 integer linear programming model, which can be efficiently solved by typical commercial solvers based on a linear integer programming framework. Numerical results demonstrate that the solution time of the proposed linearized model is lower than that of existing nonlinear programming models, and the diagnostic accuracy of the proposed model is higher than that of other state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

Lin,

Wang,

et al. 2024

IEEE Access

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Identification of transmission line voltage sag sources based on multi‐location information convolutional transformer

Li,

Zheng,

Liu

et al. 2024

IET Renewable Power Gen

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Conventional methods for identifying voltage sag sources are difficult to categorize accurately due to the complexity of transmission lines and the influence of noise. In order to solve the problem of difficulty in recognizing voltage sag sources of transmission lines under different locations, this paper proposes a new method for transmission line fault diagnosis based on modified wavelet denoising and multi‐location information convolution transformer. The improved wavelet denoising method proposed in this paper solves the problems of discontinuity and bias of the traditional wavelet denoising method, and is able to better reconstruct the original voltage signal in a strong noise environment. In addition, multi‐location information convolution transformer adopts a new model combining multi‐location information convolution and multi‐scale convolution transformer, which realizes the combination of global information and local context information capturing ability under multi‐location faults. This paper validates the method through simulation experiments and practical situations, and the results show that the method can well classify and identify the type and location of voltage sag sources in transmission lines.

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GIS Partial Discharge Pattern Recognition Based on IWOA-SVM Algorithm

Wang,

Fang,

Kou

et al. 2024

J. Phys.: Conf. Ser.

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To effectively recognize partial discharge (PD) types in gas-insulated switchgear (GIS) equipment, a novel PD pattern recognition method is proposed. This method leverages an improved whale optimization algorithm (IWOA) to optimize the support vector machine (SVM). Firstly, typical PD defects were simulated in the GIS chamber. The PRPS results demonstrated significant differences among four PD defects. Secondly, multiple feature values of PD results were calculated for feature extraction. Finally, the IWOA-SVM algorithm was employed for the recognition of PD patterns in GIS. Experimental results indicate that the IWOA-SVM algorithm can effectively recognize different PD types and the predicted accuracy rate can reach 99.1667%, which is much higher than SVM and WOA-SVM algorithms, providing information to construction units for maintenance.

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Transmission Line Fault Diagnosis Method Based on Improved Multiple SVM Model

Cited by 5 publications

References 29 publications

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

Identification of transmission line voltage sag sources based on multi‐location information convolutional transformer

GIS Partial Discharge Pattern Recognition Based on IWOA-SVM Algorithm

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