A Smart and Effective Energy Management System for Shipboard Applications using a Stochastic Fractal Search Network (SFSN) Controlling Model
Keywords:
Hybrid Renewable Energy Sources, Fuel Cell, Battery Storage, Non-Isolated High Gain Converter, Inverter, Bi-Directional Converter, Shipboard, Stochastic Fractal Search Network (SFSN) Controller, Energy Management System (EMS)Abstract
The use of all-electric ships is a recent emerging technology due to the growing effects of ship pollution on the environment and the preventive legislation that are tightening every day. Fuel cells are a promising technology, making it an intriguing decision for marine vessels to use them as their primary energy source. The primary goal of this research effort is to facilitate the development of a new energy management system to meet the load requirements of ship board applications. In this work, a sophisticated controlling mechanism called Stochastic Fractal Search Network (SFSN) has been developed to achieve the aforementioned objective. In this hybridized system, the fuel cell serves as the primary source of energy while the battery storage serves as a supplementary storage device. Additionally, this work implements two distinct converter topologies, including a non-isolated high gain interleaved converter for fuel cells and a bi-directional converter for battery storage. These converters are primarily used for effectively raising the output voltage of hybridized energy sources while minimizing ripple switching stress, voltage loss, and distortions. The proposed SFSN combines Deep Neural Network (DNN) and Stochastic Fractal Search (SFS) optimization techniques to anticipate the fuel cell's output power. In order to control energy effectively on an electric ship board, the DNN technique collects the input parameters of load demand power and battery SoC during this process. With the help of the SFS algorithm, the bias and weight values of the DNN are optimally computed in this approach. The proposed SFSN's main advantages are improved efficiency, efficient utilization of energy in accordance with load requirements, and dependability for ship applications. In the simulation analysis, the normal, high, and low battery SoC states are used to determine the load demand and fuel cell power. Also, some other measures including fitness, converter’s voltage gain and efficiency are also validated and compared in this assessment. According to the results, the suggested SFSN can efficiently monitor and control the energy requirements of electric ships with a hybridized energy system.
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