Neural network optimal-power-flow

The paper develops a massively-parallel computing structure based on arrays of neural networks to solve the optimal power flow (OPF) problem. The context of its application is in EMS (energy management systems) relating to finding optimal operating states for an interconnected power network system where the total load demand which the system is required to supply is specified. A principal feature of the neural network OPF is that it offers ultra-highspeed computation. It provides parallel computation and, at the same time, takes full advantage of sparsity of the matrixes encountered in ORE.