Optimization of a load control system algorithm for electrically propelled ships using data mining

The international maritime organization is reinforcing regulations on ship emissions and carbon dioxide, such as sulfur and nitrogen oxides. To solve this, electric propulsion vessels can use a battery-controlled load control system (LCS) to satisfy emission regulations. In this study, load data mea...

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Bibliographic Details
Published in:SN applied sciences 2021-04, Vol.3 (4), p.475, Article 475
Main Authors: Lee, Jong-Hak, Lee, Hun-Seok, Oh, Jin-Seok
Format: Article
Language:English
Subjects:
3. Engineering (general)
Algorithms
Applied and Technical Physics
Boolean
Carbon dioxide
Chemistry/Food Science
Control systems
Data collection
Data mining
Design
Earth Sciences
Efficiency
Electric propulsion
Electricity distribution
Emission analysis
Emission measurements
Emissions
Energy management
Engineering
Environment
Environmental impact
Environmental regulations
Flexibility
Generators
Materials Science
Nitrogen oxides
Optimization
Photochemicals
Power supply
Regulations
Research Article
Self organizing maps
Simulation
Sulfur
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Summary:The international maritime organization is reinforcing regulations on ship emissions and carbon dioxide, such as sulfur and nitrogen oxides. To solve this, electric propulsion vessels can use a battery-controlled load control system (LCS) to satisfy emission regulations. In this study, load data measured for one year on a ship is analyzed using a self-organizing map (SOM). Based on the analysis results, the optimal power generation capacity and battery capacity are designed, and a LCS algorithm is built. Model the ship's power generation system using MATLAB Simulink to verify the algorithm and verify its effectiveness. The LCS algorithm proposed in this study has a 1.01% reduction in fuel consumption and a 10.97% battery charge compared to the existing LCS algorithm.
ISSN:2523-3963
2523-3971
DOI:10.1007/s42452-020-04126-7