A Comprehensive Evaluation Model for the State of Electric Energy Metering Devices Based on Fuzzy Analytic Hierarchy Process - Xu, Chen; Chao, Zhang; HaoMiao, Zhang; YingChun, Su; Yu, Yan; YinZhe, Xu (Informatica, 2025)
Accurately evaluating the status of electric energy metering devices is the foundation for ensuring their stable operation on smart grids, and is conducive to the development of equipment management towards refinement and intelligence. This article proposes a comprehensive evaluation model through the fuzzy analytic hierarchy process (F-AHP), which is characterized by establishing a multi-index system and taking into account subjective opinions and objective data, thereby improving the scientificity of its evaluation and enhancing its anti-interference ability. It starts with establishing a hierarchical structure, dividing the functions of indicators such as structural reliability, measurement accuracy, communication stability, and environmental adaptability. Then, based on the fuzzy decision matrix assignment, the importance of each indicator is calculated, and the assignment and overall score of the indicators are obtained, completing the quantitative evaluation of the health of the measuring device. In the experimental verification, 50 typical electric energy metering device samples were selected for state evaluation modeling. The average CI value of the model was 0.016, the coefficient of variation CV was 0.069, and the accuracy of state recognition reached 92.5%. The evaluation results have high stability, can effectively identify boundary fuzzy samples, and have strong robustness and practical value. The results indicate that the evaluation model proposed in this article can better solve multiple practical cases and the overall evaluation error does not exceed 5%; Compared with traditional AHP and weighted average method (WAM), this model performs better in state recognition accuracy and boundary blurring processing ability. Simultaneously conducting noise experiments and sensitivity analysis, and proving that the model has high stability and reliability under various abnormal conditions.