风力轮机变速箱的智能操作和维护通过数字双胞胎和多源数据融合
Tiantian Xu1, Xuedong Zhang1, Wenlei Sun1
1School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830046, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了一种使用数字双胞胎和WOA-TCN-Attention模型的智能风力轮机运行和维护 (O&M) 方法. 它实现了准确的故障预测和早期警告,减少了停机时间和成本.
科学领域:
- 可再生能源工程可再生能源工程
- 在工程领域的人工智能.
- 预测性维护是指预测性维护.
背景情况:
- 风力轮机运行和维护 (O&M) 受到设备复杂性,恶劣环境和实时故障预测方面的困难的挑战.
- 传统的O&M方法往往导致警告延迟,导致停机时间和维护费用增加.
- 有效的O&M对于风能基础设施的效率和寿命至关重要.
研究的目的:
- 为风力轮机开发一个智能的O&M框架,利用数字双胞胎技术和多源数据融合.
- 设计一种先进的算法,用于分析多源操作数据,以提高故障检测.
- 为了实现实时,高效的O&M管理,并最大限度地减少运营中断.
主要方法:
- 提出了一个基于数字双胞胎技术的风力轮机远程智能O&M框架.
- 一个以鱼优化算法优化的时间卷积网络与注意力机制 (WOA-TCN-Attention) 已开发用于多源数据分析.
- 通过统计残留分析确定了轮箱故障值,并通过残留绝对值确定了异常状态.
主要成果:
- WOA-TCN-Attention模型在使用多源数据 (例如温度,压力,功率) 预测风力轮机变速箱故障方面表现出卓越的性能.
- 与基线评估 (EMAE,ERMSE,EMAPE) 相比,提出的方法实现了最低的预测误差.
- 提供了有效的早期警告,实际故障发生前的平均交付时间为18小时33分钟.
结论:
- 基于数字双胞胎的智能O&M方法显著提高了风力轮机故障预测的准确性和及时性.
- WOA-TCN-Attention模型提供了一个强大的解决方案,用于分析复杂的运营数据,并实现主动维护.
- 这些发现支持先进的人工智能和数字双胞胎技术的实施,以实现更高效,更可靠的风能运营.
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