Multicriteria Decision-Making Model for Assessing the Aging Condition of Composite Insulators Using AHP-Entroped TOPSIS

Current multidimensional parameter evaluation methods for assessing the aging condition of composite insulators face challenges such as subjective bias, objective absolutism, and data correlation interference, leading to unscientific weight allocation and inaccurate outcomes. To address these issues, this study proposes a multicriteria decision-making (MCDM) model that combines the subjective weights of the analytic hierarchy process (AHP) with the objective weights of the entropy method. The model employs the technique for the order preference by similarity to ideal solution (TOPSIS) to calculate the distance between evaluation indicators and ideal positive/negative solutions, enabling precise quantitative evaluation of the aging condition. Accelerated aging experiments on three composite formulations were conducted, and 12 key quantitative indicators were extracted from samples at various aging stages. These indicators were used to develop the MCDM-TOPSIS evaluation model. Pearson correlation coefficients (PCCs) of -0.95, -0.93, and -0.94 between aging condition scores and aging time demonstrated a strong negative correlation. Sensitivity analysis showed a model error below 3%, highlighting its robustness. Compared to other models, the proposed approach achieved a 92% accuracy rate, confirming its effectiveness. This model provides theoretical insights and practical guidance for assessing the aging conditions of composite insulators.