Fast Calculation of Probabilistic Power Flow: A Model-Based Deep Learning Approach

• Published in: IEEE transactions on smart grid Vol. 11; no. 3; pp. 2235 - 2244
• Main Authors: Yang, Yan, Yang, Zhifang, Yu, Juan, Zhang, Baosen, Zhang, Youqiang, Yu, Hongxin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Back propagation; Computer simulation; Deep learning; deep neural network; Flow equations; initialization method; Load flow; Machine learning; Mathematical model; Model-based deep learning; Neural networks; Physical properties; Power flow; Probabilistic logic; probabilistic power flow; Systems analysis; Training; Uncertainty
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2019.2950115

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.