Machine Learning Approach to Identify Environmentally Friendly Alternatives to SF6 for Electricity Networks

用于识别电力网络 SF6 环保替代品的机器学习方法

• 批准号: 2856914
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2856914
• 关键词: Machine; Learning; Approach; Identify; Environmentally

The use of sulphur hexafluoride (SF6) in modern transmission and distribution networks needs to be phased out as it is a highly potent greenhouse gas. Annually more than 8000 tonnes of SF6 are emitted into the atmosphere and 80% of it is used in the power industry, therefore its use in gas insulated equipment must be reduced as soon as possible to avoid the negative contribution to the climate change. However, SF6 has great electrical insulation properties which makes finding a substitution challenging. Due to a high number of existing gas combinations, it is impractical to perform high voltage testing on all mixtures. The machine learning approaches are known to be applied to determine the electrical properties of gases in other research, but the generation of mixtures has not been investigated yet. Hence, the intent of this project is to develop a set of tools that will narrow down the search space for SF6 alternatives with the aid of machine learning. Based on the known electrical properties of gases, their simulation and/or experimental data, machine learning will be able to find the potential candidates matching required operational properties. Then the identified gases can be further tested in a controlled laboratory environment to verify the results. Machine learning can also help fine tune a mixture of known gases for optimal performance. This approach will significantly speed up the identification process and minimise the time to market. The proposed outputs will be achieved through a modular approach to software development. Firstly, the literature about machine learning and other computational approaches will be reviewed in order to select applicable methods both for gas properties prediction and mixture generation. Secondly, a dataset of compounds with the properties of interest will be gathered to train and test the algorithms. With the dataset in place, the software for gas mixture optimisation based on their evaluated operational and environmental parameters will be developed. Lastly, the results of the main software will be tested in the HV laboratory to verify the suitability of identified gas mixtures and to fine-tune the algorithms. Besides that, the prediction of the individual compound properties from molecular descriptors obtained via first principles calculations such as density functional theory will be explored. This will make a large computational screening approach possible which will filter out unusable compounds and highlight potential SF6 replacement candidates.

在现代变速箱和分销网络中使用硫六氟化硫酸盐（SF6）需要逐步淘汰，因为它是一种高度有效的温室气体。每年将超过8000吨的SF6排放到大气中，其中80％用于电力行业，因此必须尽快减少其在绝缘设备中的使用，以避免对气候变化的负面贡献。但是，SF6具有出色的电绝缘材料，这使得找到替代方案具有挑战性。由于现有的气体组合数量很高，因此对所有混合物进行高压测试是不切实际的。已知该机器学习方法可用于确定其他研究中气体的电性能，但尚未研究混合物的产生。因此，该项目的目的是开发一套工具，这些工具将借助机器学习缩小SF6替代方案的搜索空间。根据气体的已知电气性能，它们的模拟和/或实验数据，机器学习将能够找到匹配所需操作特性的潜在候选物。然后，可以在受控的实验室环境中进一步测试确定的气体以验证结果。机器学习还可以帮助微调已知气体的混合物，以获得最佳性能。这种方法将大大加快识别过程并最大程度地减少上市时间。提出的输出将通过模块化的软件开发方法来实现。首先，将审查有关机器学习和其他计算方法的文献，以便选择用于气体性能预测和混合物产生的适用方法。其次，将收集具有感兴趣属性的化合物数据集以训练和测试算法。有了数据集，将开发基于其评估的操作和环境参数的气体混合物优化软件。最后，将在HV实验室测试主软件的结果，以验证已识别的气体混合物的适用性并微调算法。除此之外，还将探索通过第一原理计算获得的分子描述符（例如密度功能理论）对单个化合物的预测。这将使大型的计算筛选方法成为可能，该方法将滤除无法使用的化合物并突出潜在的SF6替换候选物。