Optimal Power Flow Estimation Using One-Dimensional Convolutional Neural Network

Abstract

Optimal power flow (OPF) is an important research topic in power system operation and control decision. Traditional OPF problems are solved through dynamic optimization with nonlinear programming techniques. For a large power system with large amounts of variables and constraints, the solving process would take a long time. This paper presents a new method to quickly estimate the OPF results using one-dimensional convolutional neural network (1D-CNN). The OPF problem is treated as a high-dimensional mapping between the load inputs and the generator dispatch decisions. Therefore, through training the neural network to learn the mapping between loads and generator outputs, we can directly predict the OPF results with the load information of a system. In this paper, we built and trained a 1D-CNN to learn the mappings between system loads and generator outputs, and the 1D-CNN model was tested using IEEE 30, 57, 118, and 300 Bus system. Extensive test and sensitivity study results have validated the effectiveness of using the 1D-CNN to estimate the OPF results.