As the global power grid must undergo a profound transformation in the coming decades to ensure reliable and cost-effective operation in a system with large shares of intermittent renewable energy generation, a critical element will be to leverage advanced data-driven predictive tools to optimise grid management activities. As it is expected that existing grids will be operated more to their limits, it is important to obtain better operational insights and estimations of the time to equipment failure to provide useful operational guidance and maintenance prioritisation support for grid operators. In this regard, this paper proposes a novel and real-time applicable method for fault prediction in 10 kV underground oil-insulated power cables using low-resolution data from a real case study from a Danish distribution system operator. The developed method is based on a sequence classification approach using long short-term memory neural networks where three different operational states are defined (Normal, Early warning, and Critical warning) to allow for prediction flexibility and better indication of the presence of systemic faults. Moreover, to enhance the data foundation, this paper investigates a Virtual Sample Generation method based on an adaptive Gaussian distribution. The capability of the proposed method yields satisfying results with prediction accuracy on the test set reaching as high as $\sim$90%, hence proving the usefulness of the proposed approach and paving the way for smarter maintenance protocols.

由于全球电网在未来几十年必须经历深刻的变革，以确保在具有大量间歇性可再生能源的系统中可靠且具有成本效益的运行，因此关键要素将是利用先进的数据驱动预测工具来优化电网管理活动。可以预期，现有电网将在更大范围内运行，因此，获得更好的运行洞察力和对设备故障时间的估计非常重要，以便为电网运营商提供有用的运行指导和维护优先级支持。在这方面，本文提出了一种新的实时适用的方法，该方法可使用来自丹麦配电系统运营商的真实案例研究中的低分辨率数据对10 kV地下石油绝缘电缆进行故障预测。所开发的方法基于使用长期短期记忆神经网络的序列分类方法，其中定义了三个不同的操作状态（正常，早期警告和严重警告），以允许灵活的预测并更好地指示系统故障的存在。此外，为了增强数据基础，本文研究了一种基于自适应高斯分布的虚拟样本生成方法。提出的方法的能力产生令人满意的结果，在测试集上的预测精度达到了最高。和严重警告），以提供灵活的预测并更好地指示系统性故障的存在。此外，为了增强数据基础，本文研究了一种基于自适应高斯分布的虚拟样本生成方法。提出的方法的能力产生令人满意的结果，在测试集上的预测精度达到了最高。和严重警告），以提供灵活的预测并更好地指示系统性故障的存在。此外，为了增强数据基础，本文研究了一种基于自适应高斯分布的虚拟样本生成方法。提出的方法的能力产生令人满意的结果，在测试集上的预测精度达到了最高。$〜$90％，因此证明了该方法的有用性，并为更智能的维护协议铺平了道路。