  The Optimal power flow (OPF) problem contains many constraints. However,
equality constraints along with a limited set of active inequality constraints
encompass sufficient information to determine the feasible space of the
problem. In this paper, a hybrid supervised regression and classification
learning based algorithm is proposed to identify active and inactive sets of
inequality constraints of AC OPF solely based on nodal power demand
information. The proposed algorithm is structured using several classifiers and
regression learners. The combination of classifiers with regression learners
enhances the accuracy of active / inactive constraints identification
procedure. The proposed algorithm modifies the OPF feasible space rather than a
direct mapping of OPF results from demand. Inactive constraints are removed
from the design space to construct a truncated AC OPF. This truncated
optimization problem can be solved faster than the original problem with less
computational resources. Numerical results on several test systems show the
effectiveness of the proposed algorithm for predicting active and inactive
constraints and constructing a truncated AC OPF. We have posted our code for
all simulations on arxiv and have uploaded the data used in numerical studies
to IEEE DataPort as an open access dataset.
