基于深度学习技术的太阳能阵列空间弧形图像的分析.
Afaf M Abd El-Hameed1, Ahmed S Farahat2, Khaled Y Youssef2
1National Research Institute of Astronomy and Geophysics (NRIAG), Helwan, Cairo, 11421, Egypt. mostafaafaf2000@yahoo.com.
Scientific reports
|July 27, 2025
概括
太空太阳能电池组面临着巨大的挑战. 这项研究使用深度学习和图像分析来了解弧形行为并识别损坏的太阳能电池,提高航天器的可靠性.
科学领域:
- 太空物理空间物理学
- 电气工程 电气工程 电气工程
- 材料科学是一种材料科学.
背景情况:
- 太空船太阳能电池组容易受到等离子体诱导的弧形,从而损坏组件并降低性能.
- 太阳能电池阵列的高度负偏差增加了频繁和持续的弧形事件的风险.
- 了解弧形动力学对于提高太空任务的寿命和可靠性至关重要.
研究的目的:
- 研究太阳能电池表面放电和弧形的光谱特征.
- 利用图像数据开发一个用于分析弧形行为和识别有缺陷的太阳能电池的预测模型.
- 为了了解弧形区域的空间和强度变化.
主要方法:
- 利用深度学习 (DL) 方法,包括卷积神经网络 (CNN) 和转移学习.
- 采用图像处理工具和算法 (Python,Maxim-DL) 来进行弧形图像分析.
- 分析了弧形图像,以检查弧形区域的强度和空间变化.
主要成果:
- 开发了一种强大的DL模型,用于预测弧形行为和检测缺陷细胞.
- 识别了主要发生在中间电池和互连线上的密集弧.
- 提供了关于持续弧形事件的动态和演变的见解.
结论:
- 对弧形事件的图像分析为更好地了解弧形演变提供了一条途径.
- 这些发现可以为太阳能阵列系统的改进缓解策略的开发提供信息.
- 增强对弧度的理解有助于提高航天器系统的可靠性和性能.
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