Decentralized Hopfield neural network applied to maintenance scheduling of generating units in power systems

In this paper, an efficient and practical method for solving a maintenance scheduling problem based on a neuro-computing approach is proposed. The objective of this problem is to equalize the capacity reserve over planning periods. The Dynamical Canonical Network for nonlinear programming, into which is incorporated an improved Hopfield Neural Network for linear programming by Kennedy and Chua, is modified to apply the neural network to 0-1 integer programming. To apply this approach to practical-systems, a saving of computation burden has been attempted by decomposing it into a number of subproblems (that is, the maintenance period over one year is decomposed into several sub-periods) and applying a neural network to the respective subproblems. By introducing the decomposition technique and partial modification procedure, efficient and flexible maintenance scheduling of generating units has been obtained. The proposed approach based on the extended Hopfield neural network was applied to two kinds of power system models; medium-scale (15 generators and 24 periods) and practical-scale (60 generators and 52 periods). From the results of simulation, the effectiveness of the present approach has been verified as a fast-approximation solution method for the maintenance scheduling in medium or large scale power systems.