An artificial neural net based method for power system state estimation

This paper presents a new method for power system static state estimation using an artificial neural network. The observation equation may be expressed as a set of exact quadratic equations in state estimation. Conventionally, a set of the nonlinear observation equation is linearized and successively solved by the Newton-Raphson method. In this paper, the problem is transformed into one of the quadratic minimization problems. According to the Hopfield model, this paper solves the problem that is expressed as the Lagrange function rather than the penalty function due to the accuracy of the optimal solution.