用机器学习初始化ACOPF的逐次线性规划求解器
Initializing Successive Linear Programming Solver for ACOPF using Machine Learning
摘要：
逐次线性规划（SLP）方法是求解大规模非线性优化问题的有效方法之一。解决交流最优潮流（ACOPF）问题也不例外，特别是考虑到全国各地的大型现实世界输电网络。然而，提高SLP算法的计算性能是必不可少的。实现该目标的单向式方法之一是通过具有接近最优解的算法的有效初始化。本文研究了Scikit-Learn库中可用的各种机器学习（ML）算法，以初始化SLP-ACOPF求解器，包括研究线性和非线性ML算法。我们评估这些机器学习算法中的每一个的质量，以预测潮流解决方案所需的变量。然后将该解用作SLP-ACOPF算法的初始化。该方法进行了测试的拥挤和非拥挤的3个总线系统。从最好的ML算法在这项工作中获得的结果进行比较的结果的DCOPF解决方案的SLP-ACOPF求解器的初始化。
英文摘要：
A Successive linear programming (SLP) approach is one of the favorable approaches for solving large scale nonlinear optimization problems. Solving an alternating current optimal power flow (ACOPF) problem is no exception, particularly considering the large real-world transmission networks across the country. It is, however, essential to improve the computational performance of the SLP algorithm. One way to achieve this goal is through the efficient initialization of the algorithm with a near-optimal solution. This paper examines various machine learning (ML) algorithms available in the Scikit-Learn library to initialize an SLP-ACOPF solver, including examining linear and nonlinear ML algorithms. We evaluate the quality of each of these machine learning algorithms for predicting variables needed for a power flow solution. The solution is then used as an initialization for an SLP-ACOPF algorithm. The approach is tested on a congested and non-congested 3 bus systems. The results obtained from the best-performed ML algorithm in this work are compared with the results of a DCOPF solution for the initialization of an SLP-ACOPF solver.
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