Neural Network-based Power Flow Model

TL;DR

This paper introduces a neural network model for power flow analysis that offers faster and more accurate results than traditional DC models, especially useful for renewable energy integration.

Contribution

The paper presents a novel neural network approach for power flow prediction that improves accuracy and speed over traditional methods using historical data.

Findings

Neural network model outperforms DC power flow in accuracy.

The NN model provides faster power flow estimates after training.

Enhanced analysis for renewable energy sources integration.

Abstract

Power flow analysis is used to evaluate the flow of electricity in the power system network. Power flow calculation is used to determine the steady-state variables of the system, such as the voltage magnitude/phase angle of each bus and the active/reactive power flow on each branch. The DC power flow model is a popular linear power flow model that is widely used in the power industry. Although it is fast and robust, it may lead to inaccurate line flow results for some transmission lines. Since renewable energy sources such as solar farms or offshore wind farms are usually located far away from the main grid, accurate line flow results on these critical lines are essential for power flow analysis due to the unpredictable nature of renewable energy. Data-driven methods can be used to partially address these inaccuracies by taking advantage of historical grid profiles. In this paper, a neural network (NN) model is trained to predict power flow results using historical power system data. Although the training process may take time, once trained, it is very fast to estimate line flows. A comprehensive performance analysis between the proposed NN-based power flow model and the traditional DC power flow model is conducted. It can be concluded that the proposed NN-based power flow model can find solutions quickly and more accurately than DC power flow model.