Minimum inertia demand estimation of new power system considering diverse inertial resources based on deep neural network

Abstract

With the high-proportion integration of renewable energy and power electronic equipment, the inertia supporting ability of new power system continues to decline, which seriously threatens the frequency stability of power grids. In order to clarify the operation boundary, and realise the rapid analysis and prediction of the minimum inertia demand of new power systems, this study proposes a minimum inertia demand estimation method based on deep neural network (DNN). Firstly, this study establishes the system frequency response model of new power systems containing diverse inertia resources including renewable energy, induction machine and so on. Considering the constraints of rate of change of frequency and maximum frequency deviation, the minimum inertia demand estimation model is established to ensure the system frequency stability. DNN is introduced to effectively map non-linear relations in complex situations, which can quickly estimate and predict the minimum inertia of new power systems. Adam algorithm is utilised to optimise the input weight matrix and hidden layer feature vector of the network to improve accuracy. Finally, the simulations and analysis are conducted in IEEE-39 system to verify the accuracy and generalisation ability of the proposed method in this paper.