Artificial neural network based load flow solution of Saudi national grid

This paper proposes an artificial neural network model for fast and accurate load flow analysis of practical power systems. The state-of-the-art approach for load flow analysis is based on Newton-Raphson algorithm (NRLF) or its derivatives such as fast decoupled load flow. As these methods are capable of providing the steady state solution within the specified accuracy, these techniques are effectively utilized as a planning tool by various utilities throughout the world. However, these are seen to be ineffective for on-line computations of practical large power systems because of the significant computational over-head due the inherent iterative nature of such algorithms. Even though the non-iterative DC load flow approach, derived out of NRLF is computationally faster than the conventional techniques, solution accuracy is significantly less than that of its iterative counterparts. Hence, in this paper, a fast and accurate approach is proposed based on multi-layer feed forward artificial neural network for the on-line load flow analysis. Active and reactive powers for the load buses are chosen as the inputs to the proposed ANN. The voltage magnitudes and angles at various load buses are the outputs. The effectiveness of the ANN approach is evaluated on a typical 380kV power network in Saudi Arabia. The investigations reveal that the proposed ANN based load flow approach is a potential candidate for the on-line applications in the load dispatch center.