数据集用于识别风力轮机的超低频多方向能源收获器
Julen Bacaicoa1,2, Mikel Hualde-Otamendi3,2, Mikel Merino-Olagüe1,2
1Department of Engineering, Public University of Navarre, Campus of Arrosadía 31006, Pamplona, Spain.
Data in brief
|December 17, 2024
概括
一个新的数据集有助于理解超低频振动能量收获器. 这些数据支持对风力轮机等应用程序的模型验证和性能优化.
科学领域:
- 机械工程 机械工程
- 收集能源的技术 收集能源的技术
- 数据科学用于工程的数据科学.
背景情况:
- 振动能量采集对于在偏远地区供电低功耗设备至关重要.
- 描述和优化超低频多方向振动能量收获器提出了独特的挑战.
- 现有的数据集可能无法完全捕捉到这些系统的复杂动态.
研究的目的:
- 为了呈现一个全面的,公开可用的数据集的超低频多方向振动能源收获器.
- 为了促进这些收割机的识别,表征和性能优化.
- 支持模型验证和进一步研究振动能量收集.
主要方法:
- 使用同步图像处理和信号采集进行实验数据采集.
- 录制高速视频 (240 fps) 用于内部部件的详细运动分析.
- 输入 (加速) 和输出 (能量,功率) 关系的测量.
主要成果:
- 一个包含原始输入-输出数据和后处理信息 (角度位置,速度,加速度) 的数据集.
- 振动能量收割机动态行为的详细描述.
- 在各种振动条件下在线圈中产生的测量功率输出.
结论:
- 该数据集为推进振动能量收割机研究提供了必要的实验数据.
- 允许改进低频能源采集系统的模型识别和验证.
- 支持为现实应用,包括风力轮机的收割机的优化.
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