Motor Group Aggregation of Refinery and Chemical Enterprises Based on Hierarchical Clustering Algorithm

Abstract

The electric load of the refinery and chemical enterprises is mainly induction motors. When the statistical synthesis method is used to establish the load model, it is an important research content to improve the aggregation accuracy of the motor group. The clustering feature is determined by analyzing the sensitivity of each parameter of the motor model to the power trajectory. The hierarchical clustering algorithm in machine learning is applied to the classification of the motor group, and the motor aggregation is carried out according to the classification result. Through an example, the simulation results show that, compared with the equivalent of one motor group, the hierarchical clustering algorithm can improve the accuracy of motor group aggregation.