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

中文翻译：

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混合学习辅助非活动约束过滤算法，可延长AC OPF解决时间

最佳潮流（OPF）问题包含许多约束。但是，平等约束和不平等约束的有限集合包含足够的信息来确定问题的可行空间。本文提出了一种基于混合监督监督分类学习的混合算法，可在仅基于节点功率需求信息来求解AC OPF之前预测有功和无功不等式约束。所提出的算法是通过混合使用分类器和回归学习器来构造的。所提出的算法不是直接映射需求的OPF结果，而是去除非活动约束以构建截断的AC OPF。与原始问题相比，此简化的优化问题可以用较少的计算资源更快地解决。在多个测试系统上的数值结果表明了该算法的有效性。