A Seismic Response and AdaBoost Regressor-Based Vulnerability Analysis of an ±800 kV Suspended Filter Capacitor

Existing seismic evaluations of electrical equipment in substations mainly focus on post-type equipment, with few studies addressing the suspended equipment that exhibits significant geometric nonlinearity. Most of the vulnerability analyses on substation equipment consider only ground motion uncertainty, not processing other uncertainties including material properties. Thus, this paper investigates the seismic responses of an ±800 kV suspended filter capacitor using a simulation model. A new approach for vulnerability assessment based on an adaptive boosting (AdaBoost) regressor is proposed considering the uncertainties of multiple material parameters of the suspension insulators. It is applied to the filter capacitor and validated by conventional incremental dynamic analysis (IDA). In addition, the influence of the pre-tension force of the bottommost suspension insulators is also discussed. The results indicate that increasing the pre-tension force can avoid pressure generation in the insulators and reduce the maximum displacements of the filter capacitor. The failure probability will significantly increase when the pre-tension force increases from 20 kN, although the growth rate continues to fall. The established AdaBoost regressors substantially lower the calculational cost while maintaining an accurate vulnerability prediction, compared to IDA. The proposed method is endorsed due to its high accuracy and low calculation cost, although its feasibility is validated by only one suspended filter capacitor in this paper.

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Publication Details

Journal
Applied Sciences
Published
2025-03-18
DOI
https://doi.org/10.3390/app15063314
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

A Seismic Response and AdaBoost Regressor-Based Vulnerability Analysis of an ±800 kV Suspended Filter Capacitor

Ting Yang, Quan Zhou, Chang He, Yongheng Mao et al.
Applied Sciences
Machine Fault Diagnosis Techniques
article

A Seismic Response and AdaBoost Regressor-Based Vulnerability Analysis of an ±800 kV Suspended Filter Capacitor

Ting Yang, Quan Zhou, Chang He, Yongheng Mao, Zhongkai Yin
article en

Abstract

Existing seismic evaluations of electrical equipment in substations mainly focus on post-type equipment, with few studies addressing the suspended equipment that exhibits significant geometric nonlinearity. Most of the vulnerability analyses on substation equipment consider only ground motion uncertainty, not processing other uncertainties including material properties. Thus, this paper investigates the seismic responses of an ±800 kV suspended filter capacitor using a simulation model. A new approach for vulnerability assessment based on an adaptive boosting (AdaBoost) regressor is proposed considering the uncertainties of multiple material parameters of the suspension insulators. It is applied to the filter capacitor and validated by conventional incremental dynamic analysis (IDA). In addition, the influence of the pre-tension force of the bottommost suspension insulators is also discussed. The results indicate that increasing the pre-tension force can avoid pressure generation in the insulators and reduce the maximum displacements of the filter capacitor. The failure probability will significantly increase when the pre-tension force increases from 20 kN, although the growth rate continues to fall. The established AdaBoost regressors substantially lower the calculational cost while maintaining an accurate vulnerability prediction, compared to IDA. The proposed method is endorsed due to its high accuracy and low calculation cost, although its feasibility is validated by only one suspended filter capacitor in this paper.

Applied SciencesVol. 15(6)
Central South University (CN), Baosteel (China) (CN), Imperial College London (GB), China Earthquake Administration (CN)
Natural Science Foundation of Hunan Province, Institute of Engineering Mechanics, China Earthquake Administration
Openalex Percentile: Top 95%
Machine Fault Diagnosis Techniques
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