人工神经网络基线模型在新型加湿微型燃气轮机循环状态监测中的应用

IF 1.4 4区 工程技术 Q3 ENGINEERING, MECHANICAL
Kathryn Colquhoun, Nikpey Homam, Ward De Paepe
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引用次数: 0

摘要

MGT市场仍然被认为是一个小众市场,要进一步推广该技术在分布式发电中的应用,还需要解决研发和开发等挑战。基于循环加湿概念的创新MGT循环,可以考虑获得更高的系统性能。然而,考虑到mgt安装在消费点附近,由非技术的产消者操作,他们获得维护服务的机会非常有限,他们还应该提供高可用性和可靠性,以避免意外停机并确保供应。因此,需要智能监控系统来支持非专业的最终用户在故障发生之前检测退化并计划维护。在这项研究中,我们研究并开发了基于人工神经网络(ANNs)的先进方法,用于在实际操作条件下对加湿MGT循环进行状态监测。为了创建一个高性能的模型,我们进行了大量的数据预处理,以去除数据异常值并选择最优的模型特征,从而提供最佳的结果。此外,模型的超参数,如学习率,动量和隐藏节点的数量被改变,以实现最准确的预测。本研究的结果提供了一个基线神经网络模型,能够对微湿空气涡轮(mHAT)系统进行状态监测,这将应用于未来的其他研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Application of an Artificial Neural Network as a Baseline Model for Condition Monitoring of Innovative Humidified Micro Gas Turbine Cycles
Abstract The MGT market is still considered to be niche and there are R&D&I challenges that need to be addressed to further promote this technology in distributed generation applications. Innovative MGT cycles based on a cycle humidification concept, can be considered to obtain higher system performance. However, given the fact that MGTs are installed close to the consumption points, where they are operated by non-technical prosumers with very limited access to maintenance services, they should also offer high availability and reliability to avoid unexpected outages and secure the supply. Therefore, intelligent monitoring systems are needed that can support non-expert end-users to detect degradation and plan maintenance before a breakdown occurs. In this study, we investigated and developed advanced methods based on artificial neural networks (ANNs) for condition monitoring of a humidified MGT cycle under real-life operational conditions. To create a high-performing model, extensive data preprocessing has been conducted to remove data outliers and select optimum model features, which provide best results. Additionally, the model hyperparameters such as learning rate, momentum and number of hidden nodes have been altered to achieve the most accurate predictions. The results of this study have provided a baseline ANN model capable of conducting condition monitoring of a micro-Humid Air Turbine (mHAT) system, which will be applied to additional studies in the future.
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来源期刊
CiteScore
3.80
自引率
20.00%
发文量
292
审稿时长
2.0 months
期刊介绍: The ASME Journal of Engineering for Gas Turbines and Power publishes archival-quality papers in the areas of gas and steam turbine technology, nuclear engineering, internal combustion engines, and fossil power generation. It covers a broad spectrum of practical topics of interest to industry. Subject areas covered include: thermodynamics; fluid mechanics; heat transfer; and modeling; propulsion and power generation components and systems; combustion, fuels, and emissions; nuclear reactor systems and components; thermal hydraulics; heat exchangers; nuclear fuel technology and waste management; I. C. engines for marine, rail, and power generation; steam and hydro power generation; advanced cycles for fossil energy generation; pollution control and environmental effects.
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