Full-Lifecycle Deterioration Characteristics and Remaining Life Prediction of ZnO Varistors Based on PSO-SVR and iForest

To address three core deficiencies of the existing research on ZnO varistors (incomplete full-lifecycle datasets, insufficient characterization robustness due to the lack of multi-parameter complementarity, and disconnected remaining life prediction and failure threshold determination), this study proposes a comprehensive technical solution for ZnO varistor remaining life prediction. An 8/20 μs impulse current accelerated deterioration experiment was designed to construct a full-lifecycle dataset (441 sets of data) covering nine same-batch ZnO varistors from their initial state to complete failure. Five core electrical parameters (varistor voltage U1mA, nonlinear coefficient α, leakage current IL, parallel resistance Rp, parallel capacitance Cp) were fused, and principal component analysis (PCA) was adopted for dimensionality reduction to form a high-robustness characterization feature (correlation coefficient with deterioration degree = 0.96). A combined model of Particle Swarm Optimization-Support Vector Regression (PSO-SVR) and Isolation Forest (iForest) was established to realize “quantitative prediction–qualitative threshold” collaboration. Experimental results show that the PSO-SVR model achieves high-precision remaining life prediction (test set R2 = 0.9726, MSE = 0.00142) and the iForest model accurately identifies the failure threshold (AUC = 0.984, accuracy = 95.9%). The combined model reaches an overall accuracy of 99.89%, effectively solving the core deficiencies of the existing research and providing key technical support for SPD-condition monitoring and operation and maintenance decisions in energy systems.