Title :
Real power transfer capability calculations using multi-layer feed-forward neural networks
Author :
Luo, X. ; Patton, A.D. ; Singh, C.
Author_Institution :
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA
fDate :
5/1/2000 12:00:00 AM
Abstract :
This paper proposes a neural network solution methodology for the problem of real power transfer capability calculations. Based on the optimal power flow formulation of the problem, the inputs, for the neural network are generator status, line status and load status and the output is the transfer capability. The Quickprop algorithm is used in the paper to train the neural network. A case study of the IEEE 30-bus system is presented demonstrating the feasibility of this approach. The new method will be useful for reliability assessment in the new utility environment
Keywords :
feedforward neural nets; learning (artificial intelligence); load flow; multilayer perceptrons; power system analysis computing; power transmission reliability; IEEE 30-bus system; Quickprop algorithm; generator status; line status; load status; multi-layer feed-forward neural networks; neural network training; optimal power flow; real power transfer capability calculations; reliability assessment; transfer capability output; Artificial neural networks; Feedforward neural networks; Feedforward systems; Load flow; Multi-layer neural network; Network topology; Neural networks; Neurons; Power system modeling; Power system planning;
Journal_Title :
Power Systems, IEEE Transactions on