A deep learning method for short-term dynamic positioning load forecasting in maritime microgrids

Abstract:

Featured Application Application of deep learning techniques to dynamic positioning in maritime microgrids for power management system. Abstract The dynamic positioning (DP) system is a progressive technology, which is used in marine vessels and maritime structures. To keep the ship position from displacement in operation mode, its thrusters are used automatically to control and stabilize the position and heading of vessels. Hence, the DP load forecasting is already an essential part of DP vessels, which the DP power demand from the power management system (PMS) for thrusting depends on weather conditions. Furthermore, the PMS is used to control power generation, and prevent power failure, limitation. To perform station keeping of vessels by DPS in environmental changes such as wind, waves, capacity, and reliability of the power generators. Hence, a lack of power may lead to lower DP performance, loss of power, and position, which is called shutdown. Therefore, precise DP power demand pbkp_rediction for maintaining the vessel position can provide the PMS with sufficient information for better performance in a complex decision-making process for the DP vessel. In this paper, the concept of deep learning techniques is introduced into DPS for DP load forecasting. A Levenberg–Marquardt algorithm based on a nonlinear recurrent neural network is employed in this paper for pbkp_redicting thrusters’ power consumption in sea state variations due to challenges in power generation with the relative degree of accuracy by combining weather parameter dependencies as environmental …

Año de publicación:

2020

Keywords:

Fuente:

google