Fast and reliable solvers for optimal power flow (OPF) problems are attracting surging research interest. As surrogates of physical-model-based OPF solvers, neural network (NN) solvers can accelerate the solving process. However, they may be unreliable for ``unseen" inputs when the training dataset is unrepresentative. Enhancing the representativeness of the training dataset for NN solvers is indispensable but is not well studied in the literature. To tackle this challenge, we propose an OPF solver based on a physical-model-integrated NN with worth-learning data generation. The designed NN is a combination of a conventional multi-layer perceptron (MLP) and an OPF-model module, which outputs not only the optimal decision variables of the OPF problem but also the constraints violation degree. Based on this NN, the worth-learning data generation method can identify feasible samples that are not well generalized by the NN. By iteratively applying this method and including the newly identified worth-learning samples in the training set, the representativeness of the training set can be significantly enhanced. Therefore, the solution reliability of the NN solver can be remarkably improved. Experimental results show that the proposed method leads to an over 50% reduction of constraint violations and optimality loss compared to conventional NN solvers.
翻译:最佳电流(OPF)问题快速可靠的解决者正在吸引巨大的研究兴趣。 随着基于物理模型的 OPF 解决方案的代理者,神经网络(NN)解决方案者可以加速解决进程。 但是,当培训数据集不具有代表性时,它们对“不见”投入“不可靠 ” 。 提高NNP解决方案者培训数据集的代表性是不可或缺的,但在文献中并没有很好地研究。 为了应对这一挑战,我们提议了一个基于物理模型的OPF解决方案(NNN)的解决方案,并具有值得学习的数据生成。 设计的NPN是一个常规多层渗透器(MLP)和OPFF模式模块的组合,其产出不仅是OPF问题的最佳决定变量,而且还包括违反程度的限制程度。基于这个NNN,值得学习的数据生成方法可以确定哪些可行的样本,而NNP并不十分普及。通过迭代用这种方法,将新发现的值得学习的样本纳入培训数据集中,可以大大加强培训数据集的代表性。因此,与50号模型的解决方案的可靠性可以比对50号最佳降低方法的可靠性,从而明显地显示最佳降低NFNR的失败的结果。