Construction and optimization of data model based on knowledge feature in transmission line equipment state recognition

Transmission lines are a vital component of the power system, and their operational status directly affects the safety and stability of the entire grid. This study utilizes the association rules algorithm to conduct an in-depth analysis of the historical data of the lines' status, achieving the mining of knowledge features. Based on this, a key state quantity system is constructed, and a transmission line operational status recognition model is established, combined with the random forest algorithm, and further optimized. Through the selection of association rules and confidence, 8 components and 81 indicator state parameters that can reflect the operational status of transmission lines are determined, quantified into a key parameter system. For the improved model, the accuracy of the evaluation of the four state levels is more than 90%. Through comparative experiments with classical models on the national grid data set, the applicability and rationality of the proposed classification algorithm are verified. The model based on the random forest algorithm is capable of efficiently handling large-scale data sets and high-dimensional features, and resisting overfitting in the classification task of transmission line equipment status recognition, while also effectively managing imbalanced data sets. With an increase in the amount of collected data and improvement in data quality, future research can continue to focus on optimizing the model to improve recognition accuracy.