Solving Non-linear Optimization Problem in Engineering by Model-Informed Generative Adversarial Network (MI-GAN)

Yuxuan Li, Chaoyue Zhao, Chenang Liu
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Abstract

Optimization models have been widely used in many engineering systems to solve the problems related to system operation and management. For instance, in power systems, the optimal power flow (OPF) problem, which is a critical component of power system operations, can be formulated using optimization models. Specifically, the alternating current OPF (AC-OPF) problems are challenging since some of the constraints are non-linear and non-convex. Moreover, due to the high variability that the power system may have, the coefficients of the optimization model may change, increasing the difficulty of solving the OPF problem. Although the conventional optimization tools and deep learning approaches have been investigated, the feasibility and optimality of the solutions may still be unsatisfactory. Hence, in this paper, based on the recently developed model-informed generative adversarial network (MI-GAN) framework, a tailored version for solving the non-linear AC-OPF problem under uncertainties is proposed. The contributions of this work can be summarized into two main aspects: (1) To ensure the feasibility and improve the optimality of the generated solutions, two important layers, namely, the feasibility filter layer and optimality-filter layer, are considered and designed; and (2) An efficient model-informed selector is designed and integrated to the GAN architecture, by incorporating these two new layers to inform the generator. Experiments on the IEEE test systems demonstrate the efficacy and potential of the proposed method for solving non-linear AC-OPF problems.
基于模型信息的生成对抗网络(MI-GAN)求解工程非线性优化问题
优化模型已广泛应用于许多工程系统中,用于解决系统运行和管理的相关问题。例如,在电力系统中,最优潮流(OPF)问题是电力系统运行的关键组成部分,可以用优化模型来表述。具体来说,交流OPF (AC-OPF)问题是具有挑战性的,因为一些约束是非线性和非凸的。此外,由于电力系统可能具有高可变性,优化模型的系数可能会发生变化,从而增加了求解OPF问题的难度。尽管传统的优化工具和深度学习方法已经被研究过,但解决方案的可行性和最优性可能仍然令人不满意。因此,本文基于最近发展的模型知情生成对抗网络(MI-GAN)框架,提出了一个解决不确定条件下非线性AC-OPF问题的定制版本。本工作的贡献可以概括为两个主要方面:(1)为了保证生成的解的可行性和提高其最优性,考虑并设计了两个重要的层,即可行性过滤层和最优性-过滤层;(2)通过结合这两个新层来通知生成器,设计了一个有效的模型通知选择器并将其集成到GAN体系结构中。在IEEE测试系统上的实验证明了该方法解决非线性AC-OPF问题的有效性和潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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