Optimization method of remote intelligent inspection and monitoring of substation based on population hierarchy analysis and fuzzy comprehensive evaluation

In the actual operation of remote intelligent detection and monitoring systems for substations, existing evaluation methods are difficult to accurately determine the system’s operating status, and fail to identify specific optimization targets for intelligent detection and monitoring. This has become a critical challenge in the field of remote intelligent substation monitoring. Therefore, this study proposes an optimization method for remote intelligent detection and monitoring of substations based on the group analytic hierarchy process (GAHP) and fuzzy comprehensive evaluation (FCE). Firstly, a hierarchical structure model for optimizing remote intelligent substation monitoring is constructed using GAHP. The model covers the target layer, standard layer, and scheme layer, and a judgment matrix is constructed and synthesized into a group judgment matrix. The weight vector is calculated and verified to determine the weight of each standard factor. Then, the FCE method is used to determine the set of evaluation factors and the set of evaluation levels. A fuzzy relation matrix is established based on actual operational data, and the weight vector derived from GAHP is integrated with the fuzzy relation matrix to obtain comprehensive evaluation results. Finally, based on the evaluation results, the membership degree is analyzed to identify optimization priorities, and improvement measures are proposed for factors with higher weights but lower evaluation grades, thereby comprehensively enhancing the substation’s remote intelligent detection and monitoring system. The experimental results show that the comprehensive evaluation score of the substation’s remote intelligent detection and monitoring system, calculated using the proposed method, is 49.225, which closely aligns with the actual system performance. This indicates that this method can effectively evaluate the overall status of substation remote intelligent detection and monitoring systems, precisely quantify operational variations among factors within the same evaluation grade, align with practical optimization requirements for substation remote intelligent monitoring, and ultimately enhance the system’s performance.