A dynamic Bayesian network risk assessment model for coal-fired power plants based on grey correlation and triangular fuzzy theory

This article adopts analysis methods such as Bayesian network model, grey correlation analysis, analytic hierarchy process, and three-level fuzzy method to establish a risk assessment index system and dynamic risk assessment model for coal-fired power plants. The results show that starting from the perspective of "human machine environment management",relatively complete risk assessment index system for coal-fired power plants has been determined using the grey correlation analysis method, with a total of 4 first level indicators, 15 second level indicators, and 63 third level indicators; Using Analytic Hierarchy Process to determine the weights of indicators at all levels, the calculation results are incorporated into subsequent Bayesian network models, transforming the Bayesian model from a static model to a dynamic model; Then bring the calculated results into NETICA software for calculation, obtain the original Bayesian network model, and perform reverse inference analysis and sensitivity analysis; Through comparative analysis, B1 (management personnel), B8 (equipment and facility management), B10 (work environment hazards), B14 (dual prevention mechanism management), X4 (personnel "three violations" situation), X20 (equipment change management), and X43 (vibration hazards) have significant changes. Therefore, management should be strengthened in practical operations, providing a theoretical basis for the actual production management of power plants.