State Estimation Approach Based on Hopfield Neural Networks

In this paper, a novel state estimation technique via Hopfield neural networks for power systems is presented. Predicting the state of a power system is vital to its safe, regulated, and supervised operation. Traditional state estimation with optimization algorithms becomes computational bottlenecks that demands prior knowledge of the system. In contrast, Hopfield neural networks best training solution s where it finds a solution to optimization problems and application of nonlinear connections. Hopfield neural network for state estimation in a power system is performed by establishing and applying the above concepts to IEEE 6 benchmark network system.