在使用切尔诺贝利灾难优化器的动态功率控制下,SOFC与中小企业集成,使用切尔诺贝利灾难优化器.
Sameh I Selem1, Attia A El-Fergany1, Eid A Gouda2
1Department of Electric Power and Machines, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt.
Scientific reports
|January 24, 2025
概括
切尔诺贝利灾难优化器 (CDO) 精确识别固体氧化物燃料电池 (SOFC) 参数,最大限度地减少电压错误. 这种新的方法提高了微电网中的SOFC性能和控制.
科学领域:
- 能源系统工程 能源系统工程
- 计算智能是一种计算智能.
- 材料科学 材料科学 材料科学
背景情况:
- 固体氧化物燃料电池 (SOFC) 对于清洁能源发电至关重要,但准确的参数识别对于性能优化至关重要.
- 现有的SOFC参数估计方法经常面临精度和计算效率方面的挑战.
- 切尔诺贝利灾难优化器 (CDO) 是一种新的元启发算法,其灵感来源于辐射物理学.
研究的目的:
- 引入和验证切尔诺贝利灾难优化器 (CDO),用于识别固体氧化物燃料电池 (SOFC) 的七个未知参数.
- 为了最大限度地减少估计和测量SOFC输出电压数据集之间的平方误差 (SMSE) 的总和.
- 评估与微电网组件集成的SOFC的性能和动态行为.
主要方法:
- 这项研究使用了切尔诺贝利灾难优化器 (CDO),一种元启发式算法,来估计SOFC参数.
- CDO的程序是基于切尔诺贝利灾难辐射的物理行为,考虑质量,速度,频率和电离.
- 使用Simulink/MATLAB在稳态和动态条件下模拟和验证SOFC模型,包括与负载的集成和超导磁储能 (SMES).
主要成果:
- CDO在参数识别方面取得了显著的准确性,在800°C时获得了3.46μV2的低SMSE值,在900°C时获得7.38μV2的低SMSE值.
- 估计的SOFC电压数据集与测量数据密切匹配,验证了模型的准确性.
- 与其他算法相比,CDO在参数提取和SOFC性能验证方面表现优越.
结论:
- 切尔诺贝利灾难优化器 (CDO) 是一种高效的工具,用于在固体氧化物燃料电池 (SOFC) 中精确的参数识别.
- 拟议的框架准确地模拟了SOFC在各种操作条件下的行为,以及微电网内的负载场景.
- CDO的应用程序增强了SOFC在微电网系统中的控制和集成,改善了主动和反应电源管理.
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