Fast Calculation of Probabilistic Power Flow: A Model-based Deep Learning Approach
This work addresses computational challenges in power system analysis, but it is incremental as it builds on existing deep learning methods with specific optimizations for PPF.
The paper tackles the high computational burden of probabilistic power flow (PPF) in power systems by proposing a model-based deep learning approach that uses a deep neural network trained with physical equations, achieving improved accuracy and efficiency in simulations on standard IEEE and utility benchmark systems.
Probabilistic power flow (PPF) plays a critical role in power system analysis. However, the high computational burden makes it challenging for the practical implementation of PPF. This paper proposes a model-based deep learning approach to overcome the computational challenge. A deep neural network (DNN) is used to approximate the power flow calculation and is trained according to the physical power flow equations to improve its learning ability. The training process consists of several steps: 1) the branch flows are added into the objective function of the DNN as a penalty term, which improves the approximation accuracy of the DNN; 2) the gradients used in the back propagation process are simplified according to the physical characteristics of the transmission grid, which accelerates the training speed while maintaining effective guidance of the physical model; and 3) an improved initialization method for the DNN parameters is proposed to improve the convergence speed. The simulation results demonstrate the accuracy and efficiency of the proposed method in standard IEEE and utility benchmark systems.