Dynamic characteristics clustering of electric loads based on Kohonen neural network

Abstract

The characteristics clustering of electric loads are of great importance to the Measurement-Based Modeling method. In this paper, a new method based on Kohonen self-organization neural network is presented for the characteristics clustering of dynamic loads. At first, the model of every group of load disturbance data is established, and then the responses of the load models to the same voltage excitation and the pre-disturbance active power of the loads are incorporated into the feature vectors. At last, Kohonen neural network is introduced to cluster. Many sets of load data measured from North China Power System in three years (1996–1998) have been dealt with using the method. The results show load characteristics have rule though they are random and time-varying. The feasibility of the Measurement-Based Modeling approach is also proved.