Data Driven Approach towards More Efficient Newton-Raphson Power Flow Calculation for Distribution Grids

University of Twente Research Information

Abstract

Power flow (PF) calculations are fundamental to power system analysis to ensure stable and reliable grid operation. The Newton-Raphson (NR) method is commonly used for PF analysis due to its rapid convergence when initialized properly. However, as power grids operate closer to their capacity limits, ill-conditioned cases and convergence issues pose significant challenges. This work, therefore, addresses these challenges by proposing strategies to improve NR initialization, hence minimizing iterations and avoiding divergence. We explore three approaches: (i) an analytical method that estimates the basin of attraction using mathematical bounds on voltages, (ii) a data-driven model leveraging supervised learning and physics-informed neural networks (PINNs) to predict optimal initial guesses, and (iii) a reinforcement learning (RL) approach that incrementally adjusts voltages to accelerate convergence. These methods are tested on benchmark systems. This research is particularly relevant for modern power systems, where high penetration of renewables and decentralized generation require robust and scalable PF solutions. Our findings provide a pathway for more efficient real-time grid operations, which, in turn, support the transition toward smarter and more resilient electricity networks.

Authors 11

1. Jerry Jinfeng Guo
   Affiliation as printed

   Alliander N.V

2. Amirhossein Heydarian Ardakani
3. Zeynab Kaseb

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology

4. Betul Mamudi

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology

5. Lindsay Spoor Aachen

   Leiden University

   Affiliation as printed

   Leiden University

6. Farzad; id_orcid 0009-0000-7865-5822 Vazinram
   Affiliation as printed

   Datamanagement & Biometrics

7. Ugochukwu Orji

   Tilburg University

   Affiliation as printed

   Tilburg University

8. Jochen Stiasny

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology

9. Pedro P. Vergara

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology

10. Tony Xiang
    Affiliation as printed

    Alliander N.V

11. Shengyuan Yan

    Eindhoven University of Technology

    Affiliation as printed

    Eindhoven University of Technology