  AC optimal power flow (AC-OPF) problems need to be solved more frequently in
the future to maintain stable and economic operation. To tackle this challenge,
a deep neural network-based voltage-constrained approach (DeepOPF-V) is
proposed to find feasible solutions with high computational efficiency. It
predicts voltages of all buses and then uses them to obtain all remaining
variables. A fast post-processing method is developed to enforce generation
constraints. The effectiveness of DeepOPF-V is validated by case studies of
several IEEE test systems. Compared with existing approaches, DeepOPF-V
achieves a state-of-art computation speedup up to three orders of magnitude and
has better performance in preserving the feasibility of the solution.
