Electrostatic field features on the shortest interelectrode path and a SVR model for breakdown voltage prediction of rod–plane air gaps

Qiu, Zhibin; Ruan, Jiangjun; Xu, Wilsun; Wang, Xuezong; Huang, Daochun

doi:10.1049/iet-smt.2018.0058

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Therefore, the support vector regression machine is used to construct the inversion model of top oil temperature rise. Besides, the K-fold cross-validation training method and grid search (GS) parameters optimization method are used to find the optimal parameters of the SVR model [30,31]. 11.…”

Section: Identification Methods Of Transformer Abnormal Heating Statementioning

confidence: 99%

Inversion detection method of oil‐immersed transformer abnormal heating state

Deng

Ruan

Dong

et al. 2022

IET Electric Power Appl

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Transformer overheating faults have an important impact on the transformer safe and stable operation. Taking 10 kV oil‐immersed transformer as the research object, using the indirect coupling analysis method of electromagnetic thermal fluid multi‐physical field, the transformer heating characteristics under overheating faults, such as winding inter‐turn short circuit and core multi‐point grounding, are analysed, and the transformer abnormal heating state identification method is proposed based on the inversion of transformer top oil temperature rise. By inverting the transformer normal top oil temperature rise under different load rates and comparing it with the actual measured value, taking into account the top oil temperature rise inversion error, it is determined that when the top oil temperature rise measured value is higher than the inversion value by 2.9 K, it indicates the transformer is in an abnormal heating state, and the higher the deviation degree is, the deeper the degree of abnormal heating fault. The accuracy of the transformer abnormal heating state identification method is verified by the multi‐working condition temperature rise test, which can provide a certain reference for the detection and maintenance of transformer abnormal internal heating faults.

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Section: Identification Methods Of Transformer Abnormal Heating Statementioning

confidence: 99%

Inversion detection method of oil‐immersed transformer abnormal heating state

Deng

Ruan

Dong

et al. 2022

IET Electric Power Appl

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“…The prediction of air flashover with the help of machine learning algorithms can be found in [11]- [13]. The method utilized in these papers is primarily based on feature extraction using the finite element method (FEM), and the same is employed to develop a novel model for the required geometric configurations of medium gap lengths in our work.…”

Section: Prediction Methodologymentioning

confidence: 99%

Breakdown Voltage Prediction for Sphere and Semispheroid Geometries With Gaussian Process Regression-Based Model Under the Application of Lightning Impulses of Both Polarities

Vidya¹,

Sunitha

Kumar

et al. 2022

IEEE Can. J. Electr. Comput. Eng.

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The design of high-voltage (HV) systems is principally dependent on the discharge voltage of their insulation. Sphere geometry and semispheroid geometry are extremely important in HV systems, such as ground rods and gas-insulated substations (GISs). Hence, in this work, a machine learning algorithm is proposed to develop a model to predict the discharge characteristics of air for sphere and semispheroid geometries. Finite element method (FEM) simulations have been performed to extract different electric fields and energy features of air gaps in the range of 5-40 mm under lightning impulses of both polarities. While developing the model, these features along with gap lengths are considered. The features have been used for training a machine learning algorithm based on the Gaussian process regression (GPR) to develop the model. The outcomes received from the model are ratified with measured experimental data. A good comparison between the two establishes the fidelity of the novel model. The proposed methodology is also compared with the other state-of-the-art techniques and found good. Remarkable performance has been acquired for other gap geometries as well.

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“…In addition to the above three transient feature quantities, the transformer real time load rate, and 9 temperature measuring points on the iron shell were also selected as the features of the SVR model. Based on the maximum minimum method [26], all the features are normalized within the range [0, 1]. The specific locations of the 9 temperature measuring points on the iron shell are shown in Table 2 and Fig.…”

Section: ) Load Shift Factormentioning

confidence: 99%