A Hybrid Model for Health Index Classification of Metal Oxide Arresters

Metal oxide arresters (MOAs) are protective devices widely used in power transmission and distribution systems to clamp transient overvoltages caused by lightning strikes and switching events, thereby protecting electrical equipment and enhancing power system reliability. However, failure of an MOA could significantly compromise the insulation coordination and overvoltage protection of power systems, thus exposing critical assets to potentially damaging voltage stresses. Accurate and early assessment of the health condition of MOAs is essential for maintaining power system reliability and minimising the risk of catastrophic insulation failure. Owing to several limitations associated with model-based condition-monitoring techniques of MOAs, this paper investigates whether combining the temporal learning capability of Long Short-Term Memory (LSTM) with the discriminative strength of Support Vector Machine (SVM) can provide a more effective and computationally feasible approach for MOA health assessment. Therefore, a dataset comprising 5157 observations extracted from historical inspection records of 20 kV distribution surge arresters was used to train predictive models for assessing the degradation condition of MOAs. The historical inspection measurements reflect the operational condition and degradation characteristics of MOAs. The results demonstrate that the hybrid LSTM–SVM model achieved the highest predictive performance, with an R2 of 0.916 and an F1-score of 0.9711, outperforming the standalone LSTM and SVM models. However, this improvement is achieved at the expense of increased computational requirements, with the hybrid model requiring 367.8 s of training time and 548 MB of peak memory, which are higher than that observed for LSTM and SVM. Despite its higher training cost, the LSTM–SVM model achieved an inference time of 1.45 ms/sample, which is lower than that of LSTM, although higher than that of SVM. Statistical validation of the LSTM–SVM model based on the Diebold–Mariano (DM) test yields a p-value < 0.001 and negative DM statistic, providing statistical evidence of improved predictive performance. These findings suggest that the LSTM–SVM model could enable earlier identification of deteriorating MOAs, thereby supporting timely maintenance interventions and reducing the likelihood of unexpected failures and associated power supply disruptions.