论文标题
不确定和随时间变化的负载模型的分层时间和空间聚类
Hierarchical Temporal and Spatial Clustering of Uncertain and Time-varying Load Models
论文作者
论文摘要
负载建模由于其不确定和时变特性而困难。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。但是，负载建模结果的大数据集成为一个新问题。在本文中，提出了负载模型的分层时间和空间聚类方法，之后，大尺寸负载模型数据集可以由多种代表性负载模型（RLMS）表示。在时间聚类阶段，一个负载总线的RLM通过聚类拾取，以表示不同时间的负载总线的所有负载模型。在空间聚类阶段，所有负载总线的RLMS形成了一个新集合，并且系统的RLMS通过空间聚类拾取。这样，大量负载模型由少数RLM表示，通过该模型，负载模型的存储空间大大减少了。 IEEE 39总线系统中的验证结果表明，用RLM替换负载模型后仍然可以保持模拟精度。通过这种方式，验证了提出的层次聚类框架的有效性。
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.
