Improve Estimation and Operation of Optimal Power Flow(OPF) Using Bayesian Neural Network

The future of development and design is impossible without study of Power Flow(PF), exigency the system outcomes load growth, necessity add generators, transformers and power lines in  power system. The urgency for Optimal Power Flow (OPF) studies, in addition to the items listed for the PF and in order to achieve the objective functions. In this paper has been used cost of generator fuel, active power losses network and system loadability index, so artificial neural network used to compare two propagation algorithms of this type of network and define the model OPF analysis is carried out. The performances of the two algorithms are analyzed and compared using the model assessment index and MGN tests. Statistical method of Bootstrap has been used on achieve the best performance to improve OPF estimates. In order to reduce steps with less than 1%, for evaluate and improve the OPF estimation with single-objective optimization functions of the Bayesian and Perceptron neural networks have been studied in IEEE 30 bus test system. The results show the effective role of bootstrapped Bayesian neural network in terms of performance, using MATLAB software.