Neural network application to state estimation computation
Tomoyuki Nakagawa, Yasuhiro Hayashi, Shinichi Iwamoto · 2002
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.>