Multi-Source Information Fusion for Degradation Assessment of Metal-Oxide Surge Arresters in Power Systems.
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| Title: | Multi-Source Information Fusion for Degradation Assessment of Metal-Oxide Surge Arresters in Power Systems. |
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| Authors: | Zhong, Dantian1 (AUTHOR), Cai, Zhiyuan1 (AUTHOR) caizhiyuan111@126.com |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 10, p2450. 29p. |
| Subject Terms: | *Multisensor data fusion, *Convolutional neural networks, *Long short-term memory, *Electric power systems, *Optimization algorithms |
| Abstract: | As the scale of ultra-high-voltage (UHV) and extra-high-voltage (EHV) transmission networks continues to expand, the operational reliability of surge arresters has become increasingly important for power-system security. Based on equivalent degradation experiments conducted on a 1000 kV class UHV surge arrester, this study proposes a multi-source information fusion approach for degradation-state assessment. Leakage-current, UHF partial-discharge, voltage, and temperature-field data were jointly used to construct a hybrid framework integrating a multi-branch convolutional neural network (CNN) and a long short-term memory (LSTM) network. To improve model performance, the sparrow search algorithm (SSA) was introduced for hyperparameter optimization. Experimental results show that the proposed method achieved accuracies of 97.47% and 94.23% on the training and test sets, respectively, and was able to distinguish the normal condition from different degraded-section conditions under the laboratory-emulated equivalent degradation scenario considered in this study. These results indicate that multi-source information fusion combined with data-driven hyperparameter optimization is a feasible approach for laboratory-scale degradation assessment of surge arresters and provides a basis for further validation under more realistic service conditions. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | As the scale of ultra-high-voltage (UHV) and extra-high-voltage (EHV) transmission networks continues to expand, the operational reliability of surge arresters has become increasingly important for power-system security. Based on equivalent degradation experiments conducted on a 1000 kV class UHV surge arrester, this study proposes a multi-source information fusion approach for degradation-state assessment. Leakage-current, UHF partial-discharge, voltage, and temperature-field data were jointly used to construct a hybrid framework integrating a multi-branch convolutional neural network (CNN) and a long short-term memory (LSTM) network. To improve model performance, the sparrow search algorithm (SSA) was introduced for hyperparameter optimization. Experimental results show that the proposed method achieved accuracies of 97.47% and 94.23% on the training and test sets, respectively, and was able to distinguish the normal condition from different degraded-section conditions under the laboratory-emulated equivalent degradation scenario considered in this study. These results indicate that multi-source information fusion combined with data-driven hyperparameter optimization is a feasible approach for laboratory-scale degradation assessment of surge arresters and provides a basis for further validation under more realistic service conditions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19102450 |
