We present a systematic comparison between neural network (NN) architectures for inference of AC-OPF solutions. Using fully connected NNs as a baseline we demonstrate the efficacy of leveraging network topology in the models by constructing abstract representations of electrical grids in the graph domain, for both convolutional and graph NNs. The performance of the NN architectures is compared for regression (predicting optimal generator set-points) and classification (predicting the active set of constraints) settings. Computational gains for obtaining optimal solutions are also presented.
翻译:我们系统地比较神经网络(NN)结构,以推断AC-OPF解决方案。利用完全连接的NP作为基线,我们通过在图形域内对电网进行抽象的表述,为进化式和图形式NP提供模型中利用网络地形的功效。将NN结构的性能与回归(预示最佳发电机定点)和分类(预示一系列主动制约因素)设置进行比较。还介绍了获得最佳解决方案的计算收益。