Distribution System Power-Flow Solution by Hierarchical Artificial Neural Networks Structure

TL;DR

This paper introduces a hierarchical neural network approach for fast, modular, and parallel power flow solutions in distribution systems, replacing traditional methods with a machine learning-based framework.

Contribution

A novel hierarchical ANN architecture for distribution system power flow that enables fast, parallel, and modular computation, improving over classical methods.

Findings

Accurate power flow predictions for IEEE-123 and EPRI Ckt5 systems.

Significantly reduced solution times due to parallel processing.

Effective hierarchical clustering aligns with electrical correlations.

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.