基于多策略适应性COA和改进的加权核ELM的故障诊断模型:风力轮机叶片结冰的案例研究
Xingtao Wu1, Yunfei Ding1, Ruizhi Zhao2
1School of Electrical Engineering, Shanghai Dianji University, Shanghai, China.
PloS one
|August 28, 2025
概括
使用多策略适应式Coati优化算法 (MACOA) 和改进的加权内核极端学习机器 (IWKELM) 的新模型提高了风力轮机叶片结冰故障检测的准确性和速度. 这种方法提高了风能系统的诊断可靠性.
科学领域:
- 可再生能源系统
- 工程中的人工智能
- 缺陷诊断和预测
背景情况:
- 风力轮机叶片的结冰对发电效率和运行安全有重大影响.
- 精确和快速的结冰故障诊断对于有效的风力轮机管理至关重要.
- 现有的诊断方法可能缺乏实时应用所需的准确性和速度.
研究的目的:
- 开发一个先进的风力轮机叶片结冰故障的诊断模型.
- 提高现有机器学习模型的优化性能和诊断准确性.
- 引入一种新的方法,将元启发优化与机器学习相结合,用于故障检测.
主要方法:
- 提出了一种多策略适应式Coati优化算法 (MACOA),具有诸如混乱映射Lévy航班和改进的目标功能等增强功能.
- 通过优化加权参数来考虑样本分布,开发了一个改进的加权内核极端学习机器 (IWKELM).
- 将MACOA与IWKELM集成并使用随机森林 (RF) 来减少维度,以创建MACOA-IWKELM诊断模型.
主要成果:
- 在两个真实世界SCADA数据集上,MACOA-IWKELM模型实现了92.22%和96.94%的高诊断精度.
- 在50次实验中显示了精度的低标准偏差 (2.53%和1.92%),表明模型的稳定性.
- 进行比较的实验证实了所提出的模型比其他诊断方法的性能优越.
结论:
- MACOA-IWKELM模型为风力轮机叶片结冰故障的检测提供了强大而准确的解决方案.
- 集成先进的优化算法与机器学习显著提高了诊断能力.
- 这项研究有助于通过改进故障检测来提高风能系统的可靠性和效率.
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