Research on Constant Power Loads Stability of DC Microgrid Based on Machine Learning

Abstract

Constant power loads (CPLs) in the DC microgrids will lead to the instability of the bus voltage, so the power variation range needs to be limited. In this paper, a based on machine learning critical value prediction method is proposed for CPLs. Firstly, Pearson correlation analysis is used to find the factors that have effects on CPLs critical value in terms of droop coefficient and bus voltage. Then, support vector machine and Gaussian process regression prediction model of CPLs critical value are established. Finally, different scenarios of DC microgrid are established to verify the proposed algorithms. The results show that machine learning algorithms can accurately predict the critical value of CPLs, and compared with support vector machine, Gaussian process regression method has higher prediction accuracy and universality.