Steady-state deduction methods of a power system based on the prediction of large-scale wind power clusters

Abstract

The integration of a high proportion of wind power has brought disorderly impacts on the stability of the power system. Accurate wind power forecasting technology is the foundation for achieving wind power dispatchability. To improve the stability of the power system after the high proportion of wind power integration, this paper proposes a steady-state deduction method for the power system based on large-scale wind power cluster power forecasting. First, a wind power cluster reorganization method based on an improved DBSCAN algorithm is designed to fully use the spatial correlation of wind resources in small-scale wind power groups. Second, to extract the temporal evolution characteristics of wind power data, the traditional GRU network is improved based on the Huber loss function, and a wind power cluster power prediction model based on the improved GRU network is constructed to output ultra-short-term power prediction results for each wind sub-cluster. Finally, the wind power integration stability index is defined to evaluate the reliability of the prediction results and further realize the steady-state deduction of the power system after wind power integration. Experimental analysis is conducted on 18 wind power farms in a province of China, and the simulation results show that the RMSE of the proposed method is only 0.0869 and the probability of extreme error events is low, which has an important reference value for the stability evaluation of large-scale wind power cluster integration.