A Virtual Graph Constrained Learning Method for Power Flow Calculation

• Published in: IEEE transactions on power systems Vol. 39; no. 5; pp. 6784 - 6787
• Main Authors: Yang, Jianping, Xiang, Yue
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-09-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Consistency; Constraints; Deep learning; Flow equations; Load flow; Mathematical models; Message passing; Neural networks; Numerical analysis; Penalty function; physical consistency and interpretability; Power flow; Sensitivity; topological adaptability; virtual graph neural network; Voltage measurement
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2024.3429782

To enhance the practical consistency and interpretability of deep learning approaches in power flow (PF) calculation, this letter proposes a virtual graph constrained message passing neural network (VGC-MPNN) for PF analysis, which defines a virtual graph from the mathematical expression of variables to enhance the binding force of power flow equations. Different from the existing methods that simply adopt the form of penalty function to learn the physical constraints, the proposed method empowers the mathematical expression into the feedforward process of the neural network to ensure a consistent solution, which performs internal solution logic instead of fitting the labeled output of the Newton-Raphson solver. Numerical analysis shows that the proposed VGC-MPNN could guarantee the physical consistency of original PFEs and improve the sensitivity of physical non-convergence, while the topological adaptability is also proved by considering network variations.