One-Step Multilayer Unbalanced Three-Phase DSE for LV Distribution Networks

Abstract

Efficiently solving unbalanced three-phase power flow in distribution grids is pivotal for grid analysis and simulation. There is a pressing need for scalable algorithms capable of handling large-scale unbalanced power grids that can provide accurate and fast solutions. To address this, deep learning techniques, especially Graph Neural Networks (GNNs), have emerged. However, existing literature primarily focuses on balanced networks, leaving a critical gap in supporting unbalanced three-phase power grids.This letter introduces PowerFlowMultiNet, a novel multigraph GNN framework explicitly designed for unbalanced three-phase power grids. The proposed approach models each phase separately in a multigraph representation, effectively capturing the inherent asymmetry in unbalanced grids. A graph embedding mechanism utilizing message passing is introduced to capture spatial dependencies within the power system network.PowerFlowMultiNet outperforms traditional methods and other deep learning approaches in terms of accuracy and computational speed. Rigorous testing reveals significantly lower error rates and a notable hundredfold increase in computational speed for large power networks compared to model-based methods.

Distribution System State Estimation Using PV Separation Strategy in LV Feeders With High Levels of Unmonitored PV Generation

“…The complex nature of the bus current injections as the system states necessitates a complex formulation of DSSE algorithms. While estimating the real and imaginary portions of the states separately reduces the algorithm's complexity, it overlooks the interactions between the real and imaginary parts [12]. On the other hand, DSSE based on a direct power flow approach can linearize the estimation formulation [11].…”

“…In this article, like [32], system states are bus current injections in complex form. Based on the study in [12], the A a (k) matrix is considered to be an identity in (2) since the difference between current injections in each bus for successive time steps is characterized as white noise. This assumption is valid when there are only customers connected to each bus and the level of PV penetration is low.…”

“…Here, the new unsupervised data-driven technique is used to identify the unknown portions of the measured aggregated values on the transformer side. In this study like [12], active and reactive power measurements are available on the transformer side, which is common in most of the DNs.…”

“…On the other hand, estimating the real and imaginary parts of the states independently is not sufficient due to the existing interactions between the real and imaginary parts. Therefore, the complex-valued states and the complex statistics are proposed in [8] and [12]. The augmented complex Kalman filter (ACKF) based on the direct approach is a noniterative DSSE technique that is used to overcome complex-valued nonlinear signals with strong levels of correlations.…”

“…This method is applicable to threephase distribution networks (DNs) due to the complicated nature of the power system. Recently, a new one-step multilayer DSSE based on the ACKF and the direct load flow approach is used to have a linear estimator in DNs with limited measurement units [12].…”