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<p>Fault identification of vacuum on-load tap changers (OLTCs) is essential for reliable transformer operation and condition-based maintenance. Existing diagnostic methods often rely on single-modality feature settings or treat feature selection and SVM hyperparameter optimization as separate steps. These practices may limit the use of complementary fault information and lead to suboptimal model configurations. To address these limitations, a multimodal PKO-SVM framework is proposed by integrating vibration–acoustic feature-level fusion with joint feature selection and SVM hyperparameter optimization. A dataset containing 250 samples from five operating conditions was constructed. Vibration features were extracted to characterize mechanical impacts and transient dynamic responses, whereas acoustic features were derived to describe spectral-envelope and time–frequency texture variations during switching. In the proposed framework, PKO is used to jointly optimize the feature-selection mask and the SVM hyperparameters C and γ, thereby reducing empirical parameter dependence and balancing recognition performance and feature compactness through a unified fitness function based on mean Macro-F1 and a feature-count penalty term. Repeated experiments with an independent test set were conducted for evaluation. The PKO-SVM model using the fused vibration–acoustic feature set achieved an accuracy of 92.53% ± 2.98% and a Macro-F1 of 0.9252 ± 0.0299. Compared with acoustic-feature-only and vibration-feature-only inputs, the fused vibration–acoustic feature set improved the average accuracy by 8.43 and 2.96% points, respectively. Comparisons with MLP/BPNN, TabNet, Random Forest, GA-SVM, and XGBoost, together with feature-contribution analysis, further supported the effectiveness and interpretability of the proposed multimodal PKO-SVM framework. These results indicate that the multimodal PKO-SVM framework has the potential to provide a reliable and compact diagnostic model for online condition assessment of vacuum OLTCs.</p>

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Identification of vacuum OLTC faults using improved multimodal PKO-SVM

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Hao Cao,
  • Pengfei Jia,
  • Sheng Hu,
  • Zijie Wang,
  • Qingchuan Fan,
  • Shuaichao Li,
  • Chen Xiao

摘要

Fault identification of vacuum on-load tap changers (OLTCs) is essential for reliable transformer operation and condition-based maintenance. Existing diagnostic methods often rely on single-modality feature settings or treat feature selection and SVM hyperparameter optimization as separate steps. These practices may limit the use of complementary fault information and lead to suboptimal model configurations. To address these limitations, a multimodal PKO-SVM framework is proposed by integrating vibration–acoustic feature-level fusion with joint feature selection and SVM hyperparameter optimization. A dataset containing 250 samples from five operating conditions was constructed. Vibration features were extracted to characterize mechanical impacts and transient dynamic responses, whereas acoustic features were derived to describe spectral-envelope and time–frequency texture variations during switching. In the proposed framework, PKO is used to jointly optimize the feature-selection mask and the SVM hyperparameters C and γ, thereby reducing empirical parameter dependence and balancing recognition performance and feature compactness through a unified fitness function based on mean Macro-F1 and a feature-count penalty term. Repeated experiments with an independent test set were conducted for evaluation. The PKO-SVM model using the fused vibration–acoustic feature set achieved an accuracy of 92.53% ± 2.98% and a Macro-F1 of 0.9252 ± 0.0299. Compared with acoustic-feature-only and vibration-feature-only inputs, the fused vibration–acoustic feature set improved the average accuracy by 8.43 and 2.96% points, respectively. Comparisons with MLP/BPNN, TabNet, Random Forest, GA-SVM, and XGBoost, together with feature-contribution analysis, further supported the effectiveness and interpretability of the proposed multimodal PKO-SVM framework. These results indicate that the multimodal PKO-SVM framework has the potential to provide a reliable and compact diagnostic model for online condition assessment of vacuum OLTCs.