融合时空信息的高比例分布式光伏低压台区户变识别方法

Transformer-customer identification method in low voltage station with high proportion distributed photovoltaic based on fusing spatial-temporal information

随着低压台区内高比例户用分布式光伏接入以及终端用户规模的扩大,基于电压相关性原理与能量供需平衡原理的传统户变关系识别算法准确率已无法满足低压台区的需求。针对上述问题,提出一种融合时空信息的两阶段户变关系识别算法。首先提取用户与低压配变的空间位置信息,基于低压配变的最大供电范围,实现户变关系的一阶段识别,优化下一阶段的迭代初始值。随后根据用户与低压配变的用能时间序列,建立基于能量供需平衡的时序关联卷积模型,实现低压台区户变连接关系识别。仿真表明,相较于传统算法,该方法在提升户变关系识别准确率与降低计算复杂度上具备显著优势。

With the high proportion of household distributed photovoltaic access in low-voltage distribution system and the expansion of customer scale,the accuracy of the traditional transformer-customer relationship identification algorithm based on the principle of voltage correlation and the principle of energy supply-demand balance can no longer meet the requirement of low-voltage station.To tackle these obstacles,a two-stage household variable relationship identification algorithm integrating spatial-temporal information is proposed.Firstly,the spatial location information of customers and transformers is extracted for identifying the transformer-customer relationship based on the maximum power supply range of transformer,and the results were utilized in optimizing the initial input value of the next stage.Subsequently,according to the time series of energy consumption of transformers and customers,a time-sequential incidence convolution model based on the principle of energy supply-demand balance is established to realize transformer-customer relationship identification in low voltage station.Simulation results demonstrated that compared with the traditional identification algorithms,the proposed method shows significant advantages in improving accuracy in identifying transformer-household relationships and reducing computational complexity.