Transfer Capability Computations in Deregulated Power System Using Radial Basis Function Neural network approach

摘要

The aim of this paper is to approach to analysis the electrical transfer capability among different electricity markets using repeated power flow technique. Instead of minimizing the total cost in the conventional problem, in the paper, the transfer capability between two markets or two electricity supply and generation areas is maximized. To reduce the time required to compute transfer capabilities and also in order to take advantages of the superior speed of artificial neural network (ANN) over conventional methods, the radial basis function network (RBFN)-based approach also has been proposed in this paper. Artificial neural networks have been able to capture this nonlinearity and give good approximation of the relationship. For complete analysis, transfer capability is computed using the proposed algorithms of repeated power flow module under various operational conditions. This data is then used to train artificial neural networks to provide real term evaluation on transfer capability of that particular power system. The effectiveness of the proposed methods is investigated on a three area 30 bus system with a comprehensive set of operational limits and controls.