基于DBN和K-means聚类的配变重过载预警方法

针对配电变压器台区容量配置不合理、重过载现象频繁发生等带来的小样本精确预测问题，提出了一种新的配电变压器重过载预警方法。首先建立满足大数据样本学习要求的扩充样本池；采集配电变压器负荷数据、社会发展统计数据、气象数据等，选取造成重过载的输入特征变量，聚合形成精选的特征数据样本；进而构建重过载预警深度信念网络学习模型，通过分析重过载配变发展态势，短、中期预测预警，选取年负荷曲线进行K-means聚类分析，形成重过载预警清单，实现配电变压器安全隐患的预判。可解决配电变压器采样系统投运时间短训练样本数据不充足问题，实现对重过载配电变压器的风险防范和容量的优化调整。通过算例验证了模型预测的有效性。

Overload Warning for Distribution Transformer Based on DBN and K-means

In allusion to the defect in the small sample exact prediction brought by unreasonable allocation distribution transformer capacity as well as frequent heavy overload, a new early warning method for heavy overloaded distribution transformers was proposed. Firstly, an extended sample pool was formed to meet the learning requirement of large data samples. Secondly, by means of collecting load data of distribution transformers, social development and statistical data and meteorological data, the input characteristic variables that might impact heavy overload were rough selected, then aggregating them to form well-chosen feature data samples. And then, a deep belief network learning model for heavy overload early warning was constructed to analyze the development trend of heavy overloaded distribution transformers, and by means of early warning of short- and medium-term prediction and selecting annual load curve the K-means cluster analysis was performed to form heavy overload early warning list to implement the pre-judgment of the potential dangers of heavy overloaded distribution transformers. The proposed method could cope with the insufficient training sample data caused by the short operation time of the sampling system, the risk prevention of heavy overloaded distribution transformer as well as the optimization and adjustment of distribution transformers’ capacity could be realized. The early warning performance and the effectiveness of the proposed method are verified by calculation example.