Comparative Load Flow Analysis Using Artificial Neural Network and the Newton-Raphson Method

Accurate load flow analysis is essential for power system studies, as it ensures the system’s adaptability to dynamic operating conditions, efficiency, and stability. Although the Newton-Raphson algorithm and other conventional methods are frequently employed due to their quadratic convergence and robustness, they encounter difficulties in accommodating the real-time demands and non-linearities of contemporary power systems. This paper provides a comparative study that employs an Artificial Neural Network (ANN) model to measure load flow solutions, thereby enhancing adaptability to evolving load patterns and reducing dependence on iterative calculations. The Newton-Raphson method was employed on the IEEE-4 bus system to generate the dataset for the ANN’s training. Subsequently, the ANN was trained to calculate system variables based on load inputs. The Newton-Raphson results were compared to the calculation from the ANN model, with an emphasis on error metrics, computational efficiency, and accuracy. The ANN model effectively replicated the load flow results with minimal deviations, demonstrating its potential for real-time applications and the ability to manage complex, nonlinear relationships. The intersection of classical power system analysis methods and deep learning architectures is illustrated in this study, which underscores the latter’s advantages in computational efficiency and adaptability to the emergent challenges in modern power grids.