Multi-Source Information Fusion for Degradation Assessment of Metal-Oxide Surge Arresters in Power Systems

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.