Load modeling is difficult due to its uncertain and time-varying properties. Through the recently proposed ambient signals load modeling approach, these properties can be more frequently tracked. However, the large dataset of load modeling results becomes a new problem. In this paper, a hierarchical temporal and spatial clustering method of load models is proposed, after which the large size load model dataset can be represented by several representative load models (RLMs). In the temporal clustering stage, the RLMs of one load bus are picked up through clustering to represent all the load models of the load bus at different time. In the spatial clustering stage, the RLMs of all the load buses form a new set and the RLMs of the system are picked up through spatial clustering. In this way, the large sets of load models are represented by a small number of RLMs, through which the storage space of the load models is significantly reduced. The validation results in IEEE 39 bus system have shown that the simulation accuracy can still be maintained after replacing the load models with the RLMs. In this way, the effectiveness of the proposed hierarchical clustering framework is validated.

中文翻译：

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不确定和时变载荷模型的分层时间和空间聚类

由于其不确定性和时变特性，负载建模很困难。通过最近提出的环境信号负载建模方法，可以更频繁地跟踪这些属性。然而，负荷建模结果的大数据集成为一个新问题。在本文中，提出了一种负荷模型的分层时空聚类方法，之后大尺寸负荷模型数据集可以用几个代表性负荷模型（RLM）来表示。在时间聚类阶段，通过聚类选取一个负载总线的 RLM 来表示负载总线在不同时间的所有负载模型。在空间聚类阶段，所有负载总线的 RLM 形成一个新的集合，通过空间聚类提取系统的 RLM。这样，大的负载模型集由少量的 RLM 表示，通过它显着减少了负载模型的存储空间。在 IEEE 39 总线系统中的验证结果表明，在用 RLM 替换负载模型后，仿真精度仍然可以保持。这样，所提出的层次聚类框架的有效性得到了验证。