THE CHARACTERISTICS CLUSTERING AND SYNTHESIS OF ELECTRIC DYNAMIC LOADS BASED ON KOHONEN NEURAL NETWORK
Ying-mei Liu · Proceedings of the CSEE · 2003
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, 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. The advantages of this method include: self-learning function, rapid computation and strong type recognition. 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.The use of typical load models will improve the power system simulation veracity.