The acquisition of load curves for typical days is a very important part in the study of load characteristics for power system, which is of great significance to the development of load dispatching plan or operation control. As the load characteristics are affected by various random factors, for instance, meteorology, human production and livelihood, etc., the selection of typical days, using traditional or single clustering algorithms, has some disadvantages to describe the overall load curve. To address those drawbacks, this paper proposes a new integrated clustering method by combining the improved Probabilistic Fuzzy C-Means (PFCM) algorithm with Fuzzy Linear Discriminant method (FLDA). Firstly, the original PFCM is improved to get the improved PFCM, and it is applied to the selection of optimal number of clusters. Then, the improved PFCM is combined with FLDA, and the integrated clustering algorithm is applied to the clustering of load curves. Ultimately, the annual load data of a power grid proves that the method has several certain advantages in the selection of typical days.

10.1117/12.2682971

Cite

Citations (0)

Target Control Design for Stationary Probability Density Function of Nonlinear Stochastic System

ACTA AUTOMATICA SINICA (2012)

Zhu Chenxuan Liu Yang

Source

Cite

Citations (0)