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.

现代输配电网络中六氟化硫 (SF6) 的使用需要逐步淘汰，因为它是一种强效温室气体。每年有8000多吨SF6排放到大气中，其中80%用于电力工业，因此必须尽快减少其在气体绝缘设备中的使用，以避免对气候变化造成负面影响。然而，SF6 具有出色的电绝缘性能，这使得寻找替代品具有挑战性。由于现有气体组合数量较多，对所有混合物进行高压测试是不切实际的。众所周知，机器学习方法在其他研究中可用于确定气体的电特性，但混合物的生成尚未得到研究。因此，该项目的目的是开发一套工具，借助机器学习缩小 SF6 替代品的搜索空间。根据气体的已知电特性、其模拟和/或实验数据，机器学习将能够找到与所需操作特性相匹配的潜在候选气体。然后可以在受控实验室环境中进一步测试所识别的气体以验证结果。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。这种方法将显着加快识别过程并最大限度地缩短上市时间。拟议的产出将通过软件开发的模块化方法来实现。首先，将回顾有关机器学习和其他计算方法的文献，以便选择适用于气体性质预测和混合物生成的方法。其次，将收集具有感兴趣特性的化合物数据集来训练和测试算法。数据集到位后，将开发基于评估的操作和环境参数的气体混合物优化软件。最后，主软件的结果将在高压实验室进行测试，以验证识别的气体混合物的适用性并对算法进行微调。除此之外，还将探索通过密度泛函理论等第一原理计算获得的分子描述符来预测单个化合物的性质。这将使大型计算筛选方法成为可能，从而过滤掉不可用的化合物并突出潜在的 SF6 替代候选物。