Energy Consumption Analysis and Optimization of the Deep-Sea Self-Sustaining Profile Buoy

Mingcong Liu,Hongyu Li,Shaobo Yang,Xingfei Li,Jiayi Xu

doi:10.3390/en12122316

Mingcong Liu, Hongyu Li + Show 3 more

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https://doi.org/10.3390/en12122316

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Abstract

In order to reduce the energy consumption of deep-sea self-sustaining profile buoy (DSPB) and extend its running time, a stage quantitative oil draining control mode has been proposed in this paper. System parameters have been investigated including oil discharge resolution (ODR), judgment threshold of the floating speed and frequency of oil draining on the energy consumption of the system. The single-objective optimization model with the total energy consumption of DSPB’s ascent stage as the objective function has been established by combining the DSPB’s floating kinematic model. At the same time, as the static working current of the DSPB can be further optimized, a multi-objective energy consumption optimization model with the floating time and the energy consumption of the oil pump motor as objective functions has been established. The non-dominated sorted genetic algorithm-II (NSGA-II) has been employed to optimized the energy consumption model in the ascent stage of the DSPB. The results showed that the NSGA-II method has a good performance in the energy consumption optimization of the DSPB, and can reduce the dynamic energy consumption in the floating process by 28.9% within 2 h considering the increase in static energy consumption.

Highlights

In order to accurately measure the profile data of the global ocean such as temperature and salinity, a global ocean observation and experimental project “ARGO plan” [1,2,3] has been carried out in 2000.Self-sustaining profile detection buoys including the deep-sea self-sustaining profile buoy (DSPB) have been used in this project
As the energy consumption in the ascent stage accounts for a higher proportion, this paper proposes a stage quantitative oil draining control mode to reduce the dynamic energy consumption in the DSPB’s ascent stage
The first section of this chapter will analyze the result of single-objective model optimization, the second section will verify the accuracy and timeliness of the non-dominated sorted genetic algorithm-II (NSGA-II) method by traversing method, and the third section will analyze the result of single-objective model optimization and give the optimized effect compared with pre-optimization

Summary

Introduction

Self-sustaining profile detection buoys including the deep-sea self-sustaining profile buoy (DSPB) have been used in this project. The DSPB will work in the sea for more than years years until the power is exhausted and the working depth of which is up to 4000 m. The working life and operation are important for the DSPB because they involve the performance of the buoy and the reliability of the data. Effective reducing the energy consumption of DSPB’s working process is an urgent problem as the limited power supply that DSPB can carry. The number of works on energy consumption for the DSPB is relatively smaller than similar works for autonomous underwater vehicle (AUV). The working mechanism of the AUV is similar to the DSPB, so related researches of the AUV can be referenced in this paper. The energy consumption optimization of AUVs are mainly from optimizing parameters such as dynamic resistance model, gliding pitch angle and speed, the force applied by thrusters and their opening times [4,5,6,7]

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Results

Conclusion