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Hierarchical Temporal and Spatial Clustering of Uncertain and Time-varying Load Models

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Document pages: 8 pages

Abstract: Load modeling is difficult due to its uncertain and time-varying properties.Through the recently proposed ambient signals load modeling approach, theseproperties can be more frequently tracked. However, the large dataset of loadmodeling results becomes a new problem. In this paper, a hierarchical temporaland spatial clustering method of load models is proposed, after which the largesize load model dataset can be represented by several representative loadmodels (RLMs). In the temporal clustering stage, the RLMs of one load bus arepicked up through clustering to represent all the load models of the load busat different time. In the spatial clustering stage, the RLMs of all the loadbuses form a new set and the RLMs of the system are picked up through spatialclustering. In this way, the large sets of load models are represented by asmall number of RLMs, through which the storage space of the load models issignificantly reduced. The validation results in IEEE 39 bus system have shownthat the simulation accuracy can still be maintained after replacing the loadmodels with the RLMs. In this way, the effectiveness of the proposedhierarchical clustering framework is validated.

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