Improved Control of Propeller Ventilation Based on POA-XGBoost and Ship Dynamics/Control Model

Abstract

Under adverse sea conditions, propeller ventilation caused by in-and-out water can decrease the reliability of the ship power grid and the lifespan of the propulsion shaft system. Predicting the development of propeller ventilation severity while identifying it can contribute to improving propeller ventilation control. In this study, the eXtreme Gradient Boosting (XGBoost) algorithm combined with a ship dynamics/control model is proposed as a propeller ventilation identification and prediction method. Meanwhile, the Pelican optimization algorithm (POA), particle swarm optimization (PSO), and genetic algorithm (GA) are applied to determine the optimal hyperparameters of the XGBoost algorithm. The results indicate that the method can effectively identify the current propeller ventilation state and predict whether a full ventilation state will occur after experiencing a partial propeller ventilation state. The comparison results indicate that the POA has a better optimization effect on the XGBoost algorithm for propeller ventilation identification and prediction. The method proposed in this study provides crucial technical support for the effective switching of propulsion control strategies for ship electric propulsion systems under adverse sea conditions.