基于关注指标和深度学习的台区配变重过载预警方法研究

针对现行配变重过载治理方法过于被动的问题，结合电力大数据和深度学习技术提出了一种适用于大规模配电网分析的台区配变重过载预测方法。首先基于配变负载特性建立重载关注指标并建立二级过滤系统筛选出重过载风险较高的台区配变及其日期作为预测对象。然后充分考虑内外影响因素，建立卷积神经网络-门限循环单元深度学习模型实现负载率预测并转化为预警等级。实例证明了所提方法的有效性。

Heavy Overload Early Warning Method of Distribution Transformer Based on Attention Indicators and Deep Learning

Aiming at the problem that current distribution transformer overload control method is too passive，power big data and deep learning technology are combined to propose a distribution transformer overload prediction method suitable for large-scale distribution network analysis. Firstly，based on the load characteristics of distribution transformer，a heavy-load attention index is established and a secondary filtering system is established to screen out the distribution transformers and their dates with higher risk of heavy overload as the prediction objects. Secondly， fully considering internal and external factors，a convolutional neural network-gated recurrent unit deep learning model is established to achieve load rate prediction and convert it into an early warning level. An example shows the effectiveness of the proposed method.