Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time

The optimal power flow (OPF) problem contains many constraints. However, equality constraints and a limited set of inequality encompass sufficient information to determine the feasible space. This article presents hybrid supervised regression-classification learning-based algorithm predict active inactive before solving AC OPF solely based on nodal demand information. proposed is structured using mixture classifiers regression learners. Instead directly mapping results from demand, removes construct truncated OPF. optimization can be solved faster than original with less computational resources. Numerical several test systems show algorithm's effectiveness for predicting constructing