以研制的小型长航时水面无人平台为研究对象,研究波浪作用下小型长航时水面无人平台的光伏发电功率预测问题。建立基于高频非稳定姿态响应分析模型、任意斜面辐照度计算模型和光电转换模型的小型长航时水面无人平台光伏发电功率仿真分析模型,利用水池造波试验检验实海况条件平台光伏阵列姿态响应分析模型,利用陆上摇摆试验装置检验任意斜面辐照度计算模型和光电转换模型;在此基础上,梳理平台典型海上作业工况,并对各工况条件下的平台光伏发电情况进行仿真分析。研究结果表明：实海况条件下,小型长航时水面无人平台光伏瞬时发电功率波动情况受海况、波向、时刻等因素影响较大,平均功率受影响较小;3级海况下功率变化幅值最大可达平均值的82.5%,4级海况下功率变化幅值最大可达平均值的111.8%。
Abstract
Taking the developed small, long-endurance unmanned surface vehicle as the research object, the photovoltaic power generation prediction problem of unmanned surface vehicles under wave action is studied. A simulation model of photovoltaic power generation of small-scale long-duration surface autonomous observation vehicles based on high-frequency unsteady attitude response analysis model, arbitrary slant irradiance calculation model and photoelectric conversion model is established, and the attitude response analysis model of photovoltaic panels of the observation vehicle under the real sea condition is examined by using the pool wave-making test, and the calculation model of arbitrary slant irradiance and photoelectric conversion model are examined by using the on-land rocking test device. On this basis, the typical operating conditions of the observation vehicle are sorted out, and the photovoltaic power generation of the observation vehicle under each operating condition is simulated and analyzed. The results show that： under real sea conditions, the instantaneous power fluctuation of unmanned ship photovoltaic power generation is greatly affected by the sea state, wave direction, moment and other factors, and the average power is less affected; the maximum amplitude of the power change can reach 82.5% of the average value under the three-level sea conditions, and the maximum amplitude of the power change can reach 111.8% of the average value under the four-level sea conditions.
关键词
光伏发电 /
水面无人平台 /
波浪作用 /
计算机仿真 /
功率预测 /
海洋观测
Key words
PV power /
unmanned surface vehicles /
wave effects /
computer simulation /
power prediction /
ocean observation
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基金
自然资源部海洋观测技术重点实验室基金（2021klootB04）; 重点研发计划（2018YFB1503003）
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