Title: Distribution System Power-Flow Solution by Hierarchical Artificial Neural Networks Structure Show Chinese title

Title: 基于分层人工神经网络结构的配电网潮流解法

Abstract: In this paper, a new method for solving the power flow problem in distribution systems which is fast, parallel, as well as modular, straightforward, simplified and generic is proposed. This approach is based on a hierarchical construction of an ANNs tree. The power system is divided into multiple clusters, with a modular architecture. For each cluster an ANN is constructed, were the ANNs of the different clusters are organized in a hierarchical manner in which the data from a lower-level layer is fed into an upper layer in accordance with the electric correlation between the clusters. The solution time is fast as it is based on the neural networks predictions and also enables parallel computing of all clusters in any given layer. The various clusters have a uniform designed single-hidden-layer ANNs, thus providing a straightforward, simple and generic architectural implementation. The suggested methodology is an important milestone for bypassing power flow classical methods and introducing a novel machine learning based approach. The solution for three-phase unbalance IEEE-123 system as well as EPRI Ckt5 system are presented. The predictions of the ANNs of the hierarchical structures are compared to the solution as calculated by OpenDSS simulation software, with very promising results. Show Chinese abstract

Abstract: 本文提出了一种新的方法，用于解决配电系统中的潮流问题，该方法快速、并行、模块化、直接、简化且通用。 该方法基于人工神经网络树的分层结构。 电力系统被划分为多个聚类，具有模块化架构。 为每个聚类构建一个人工神经网络，不同聚类的人工神经网络以分层方式组织，其中低层的数据根据聚类之间的电气相关性输入到上层。 求解时间快速，因为它基于神经网络的预测，并且能够并行计算任何给定层的所有聚类。 各个聚类具有统一设计的单隐层人工神经网络，从而提供直接、简单且通用的架构实现。 所建议的方法是绕过传统潮流方法的重要里程碑，并引入了一种基于机器学习的新方法。 展示了三相不平衡IEEE-123系统以及EPRI Ckt5系统的解决方案。 将分层结构中人工神经网络的预测结果与OpenDSS仿真软件计算的解进行比较，结果非常有希望。