Intelligent Substation Secondary System State Evaluation Method Based on Digital Twins and Combination Weighting Method

In response to the increasingly complex secondary system of intelligent substations and the limitations of traditional evaluation methods such as data silos and single indicators, it is urgent to build a real-time high-precision status evaluation system. This article proposes a state evaluation method for the secondary system of intelligent substations based on digital twins and the combination weighting method. Firstly, a four-layer digital twin architecture consisting of physical entities, data interaction, virtual twins, and service applications is constructed to achieve precise mapping and real-time interaction of multi-source heterogeneous data in the secondary system. A comprehensive evaluation index system covering the operation status and information quality of key equipment in the secondary system of intelligent substations is built from multiple dimensions such as health status, information transmission, and environmental conditions. Next, a combined weighting evaluation model is established that integrates the subjective weights of the Analytic Hierarchy Process and the objective weights of the coefficient of variation method, and the principle of minimizing the sum of squares of subjective and objective deviations is introduced to achieve adaptive optimization of weight configuration. Finally, simulation analysis is conducted on the digital twin platform of the 220 kV intelligent substation, and the effectiveness and robustness of the proposed model are verified through multi-state evaluation, typical fault case verification, and ablation and sensitivity analysis. The results show that compared with the mainstream AHP entropy weight method, this method has a maximum deviation of 15.3% in evaluation indicators, a state evaluation score of 97.3164, a full process calculation time of about 2.6138 s, and communication delay that meets the requirements of the IEC 61850 standard. It can effectively improve the real-time accuracy of secondary system state perception and provide technical support for on-site operation and maintenance of intelligent substations.