DeepOPF-NGT: A Fast Unsupervised Learning Approach for Solving AC-OPF Problems without Ground Truth (Papers Track)
Wanjun Huang (City University of Hong Kong); Minghua Chen (City University of Hong Kong)
Abstract
AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain the reliable and cost-effective operation of power systems. Recently, supervised-learning approaches have been developed to speed up the solving time of AC-OPF problems without incurring infeasibility or much optimality loss by learning the load-solution mapping embedded in the training dataset. However, it is non-trivial and computationally expensive to prepare the training dataset with single embedded mapping, due to that AC-OPF problems are non-convex and may admit multiple optimal solutions. In this paper, we develop an unsupervised learning approach (DeepOPF-NGT) for solving AC-OPF problems, which does not require training datasets with ground truth to operate. Instead, it uses a properly designed loss function to guide the tuning of the neural network parameters to directly learn one load-solution mapping. Preliminary results on the IEEE 30-bus test system show that the unsupervised DeepOPF-NGT approach can achieve comparable optimality, feasibility, and speedup performance against an existing supervised learning approach.