In the near future, a drastic change in the structure of the electric grid is expected due to the increasing penetration of power electronics interfaced renewable energy sources (e.g. solar and wind), highly variable loads (e.g. electric vehicles and air conditioning) and unexpected energy demanding events (e.g. pandemics or natural disasters). Energy balancing management, voltage and frequency stability, reduced system inertia, grid resilience to fault conditions, and power quality of the supply are a few of the main challenges in the future power electronics dominated grids. Power electronics can solve these by integrating information and communication technology in new intelligent, highly reliable, and efficient devices like smart transformers. Smart transformers can increase the power flow flexibility by enabling the correct meshed-hybrid grid operations, as long as load mission and power generation profiles are known. Those profile are generally driven by heterogeneous, highly sparse and often incomplete data that belong to different domains. This article highlights the necessity of new approaches and models to identify patterns and events of interest that can serve as a common base. The resulting patterns can then be cross-fused in a common language and form the basis of further data analytics in future distribution grids.

在不久的将来，由于电力电子接口的可再生能源（例如太阳能和风能）、高度可变的负载（例如电动汽车和空调）和意外能源的日益普及，预计电网结构将发生巨大变化要求苛刻的事件（例如流行病或自然灾害）。能量平衡管理、电压和频率稳定性、降低系统惯性、电网对故障条件的恢复能力以及供电质量是未来电力电子主导电网的主要挑战。电力电子可以通过将信息和通信技术集成到智能变压器等新型智能、高度可靠和高效的设备中来解决这些问题。只要已知负载任务和发电曲线，智能变压器就可以通过启用正确的网状混合电网运行来增加潮流的灵活性。这些配置文件通常由属于不同领域的异构、高度稀疏且通常不完整的数据驱动。本文强调了新方法和模型的必要性，以识别可以作为共同基础的感兴趣的模式和事件。然后可以将生成的模式以通用语言交叉融合，并形成未来配电网中进一步数据分析的基础。本文强调了新方法和模型的必要性，以识别可以作为共同基础的感兴趣的模式和事件。然后可以将生成的模式以通用语言交叉融合，并形成未来配电网中进一步数据分析的基础。本文强调了新方法和模型的必要性，以识别可以作为共同基础的感兴趣的模式和事件。然后可以将生成的模式以通用语言交叉融合，并形成未来配电网中进一步数据分析的基础。