Distributed Power Grid Connected Panoramic Perception Technology Based on Digital Twin Model

Abstract

Abstract To address the issue that large-scale distributed Proton-exchange membrane fuel cell grid-connected operation status is difficult to effectively monitor, this study carried out clustering screening of Proton-exchange membrane fuel cell grid connected status variables. The digital twin model of the Proton-exchange membrane fuel cell group grid connection is established by using key important variables, and the digital model accurately reflects the real-time operation status of the Proton-exchange membrane fuel cell. Then, it predicts the operation status of the Proton-exchange membrane fuel cell. The experiment showcases that the optimal accuracy of fuzzy C-means clustering algorithm, K-means clustering algorithm and fuzzy C-means clustering algorithm in view of the objective function for clustering screening of grid-connected state quantity of Proton-exchange membrane fuel cell is 0.74, 0.65 and 0.59 respectively; The optimal recall rates are 0.84, 0.76, and 0.68, respectively; The optimal F-measure values are 0.78, 0.70, and 0.64, respectively. Among the three clustering analysis algorithms, the fuzzy C-means clustering algorithm, in view of the objective function, has the shortest running time and the least memory consumption. The monitoring results are basically consistent with the actual situation, indicating that this design method can complete the task of monitoring the operating status of distributed power grid-connected equipment. This study’s proposed method is more accurate than conventional methods and is better suited for monitoring the grid connection status of distributed power systems.