Enhancing ACPF Analysis: Integrating Newton-Raphson Method with Gradient Descent and Computational Graphs

TL;DR

This paper introduces a novel power flow analysis method that combines Newton-Raphson, Gradient Descent, and computational graphs to improve convergence speed, robustness, and adaptability in modern power systems with renewable energy sources.

Contribution

It integrates Newton-Raphson with Gradient Descent and computational graphs, eliminating Jacobian inversion and enhancing power flow analysis for variable renewable energy conditions.

Findings

Faster convergence compared to traditional methods

Consistent performance across diverse system states

Validated on IEEE benchmark systems

Abstract

This paper presents a new method for enhancing Alternating Current Power Flow (ACPF) analysis. The method integrates the Newton-Raphson (NR) method with Enhanced-Gradient Descent (GD) and computational graphs. The integration of renewable energy sources in power systems introduces variability and unpredictability, and this method addresses these challenges. It leverages the robustness of NR for accurate approximations and the flexibility of GD for handling variable conditions, all without requiring Jacobian matrix inversion. Furthermore, computational graphs provide a structured and visual framework that simplifies and systematizes the application of these methods. The goal of this fusion is to overcome the limitations of traditional ACPF methods and improve the resilience, adaptability, and efficiency of modern power grid analyses. We validate the effectiveness of our advanced algorithm through comprehensive testing on established IEEE benchmark systems. Our findings demonstrate that our approach not only speeds up the convergence process but also ensures consistent performance across diverse system states, representing a significant advancement in power flow computation.