基于稀疏自适应学习的台区用户拓扑结构校验

针对低压台区拓扑结构人工校验成本高且准确性不足的问题,提出了基于稀疏自适应学习的台区用户拓扑结构校验方法。基于用电信息系统采集的用电量数据,构建了参数化台区用电量模型,提出了稀疏自适应学习方法自动估计出模型参数。通过阈值检验识别出台户拓扑结构统计错误的用户。采用浙江省海宁地区的用电量数据对该方法的性能进行分析。实验结果表明,该方法具有较好的识别率。在模拟场景中,可以达到100%的查全率和查准率;在真实场景中,可以达到84.8%的查准率和90.7%的查全率。

Transformer area topology verification method based on sparse adaptive learning

Aiming at the problem of high cost and low accuracy using artificial verification in low-voltage transformer area topology,this paper proposes a new automatic verification method based on sparse adaptive learning.Based on the electricity consumption data of massive users,a parametric electricity consumption model of low-voltage transformer areas was constructed.Then,a sparse adaptive learning algorithm was proposed to automatically estimate the model parameters.Users who do not belong to the transformer area were identified by utilizing a threshold testing.The performance of the proposed method was tested using the electricity consumption data of a certain transformer area in Haining,Zhejiang province.Experimental results showed that the proposed method achieved a good estimation performance.In the simulative cases,the proposed method can achieve 100%accuracy ratio and 100%recall ratio.In the real cases,it can achieve 84.8%accuracy ratio and 90.7%recall ratio.