Neural network application to state estimation computation

In power systems state estimation computation takes an important role in security controls, and the weighted least squares method and the fast decoupled method are widely-used at present. State estimation computation using the existing Von-Neumann type computer is reaching a limit as far as the solution techniques are concerned, and it is very difficult to expect much faster methods. In order to solve the problem, the authors employ a neural network theory, the Hopfield network theory, which has an ultra parallel algorithm and is different from the existing calculating algorithms, for state estimation computation. A feasibility study using a 6 bus system is shown. >