Data-driven models are becoming of fundamental importance in electric distribution networks to enable predictive maintenance, to perform effective diagnosis and to reduce related expenditures, with the final goal of improving the electric service efficiency and reliability to the benefit of both the citizens and the grid operators themselves. This paper considers a dataset collected over 6 years in a real-world medium-voltage distribution network by the Supervisory Control And Data Acquisition (SCADA) system. A transparent, exploratory, and exhaustive data-mining workflow, based on data characterisation, time-windowing, association rule mining, and associative classification is proposed and experimentally evaluated to automatically identify correlations and build a prognostic–diagnostic model from the SCADA events occurring before and after specific service interruptions, i.e., network faults. Our results, evaluated by both data-driven quality metrics and domain expert interpretations, highlight the capability to assess the limited predictive capability of the SCADA events for medium-voltage distribution networks, while their effective exploitation for diagnostic purposes is promising.

中文翻译：

---

用于故障预测和诊断的配电网络数据驱动探索模型

数据驱动的模型在配电网络中变得越来越重要，以实现预测性维护、执行有效诊断和减少相关支出，最终目标是提高电力服务效率和可靠性，从而造福于市民和电网运营商自己。本文考虑了由监督控制和数据采集 (SCADA) 系统在现实世界中压配电网络中收集超过 6 年的数据集。透明、探索性和详尽的数据挖掘工作流程，基于数据特征、时间窗口、关联规则挖掘、提出并通过实验评估关联分类，以根据特定服务中断（即网络故障）前后发生的 SCADA 事件自动识别相关性并建立预测诊断模型。我们的结果通过数据驱动的质量指标和领域专家的解释进行评估，突出了评估中压配电网络 SCADA 事件的有限预测能力的能力，而它们用于诊断目的的有效开发是有希望的。