Title :
Neural network optimal-power-flow
Author_Institution :
Western Australia Univ., Nedlands, WA, Australia
Abstract :
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
Keywords :
power system analysis computing; EMS; computer simulation; energy management systems; interconnected power network; massively-parallel computing structure; neural network arrays; optimal operating states; optimal power flow; parallel computation; power systems; sparse matrix; total load demand; ultra-highspeed computation;
Conference_Titel :
Advances in Power System Control, Operation and Management, 1997. APSCOM-97. Fourth International Conference on (Conf. Publ. No. 450)
Print_ISBN :
0-85296-912-0
DOI :
10.1049/cp:19971842
