Volume 9, Issue 3 (10-2019)
JEM 2019, 9(3): 86-97 |
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Sabri M, Rezaeipour R. Improving the Estimation and Operation of Optimal Power Flow(OPF) Using Bayesian Neural Network. JEM. 2019; 9 (3) :86-97
URL: http://energy.kashanu.ac.ir/article-1-727-en.html
URL: http://energy.kashanu.ac.ir/article-1-727-en.html
Islamic Azad University of Tabriz
Abstract: (3715 Views)
The future development and design of the power system is impossible without the study of Power Flow(PF), exigency of the system outcome load growth, necessity add generators, transformers, and power lines. The urgency for Optimal Power Flow (OPF) studies, in addition to the items listed for the PF are necessary to achieve the objective functions. In this paper, the cost of generator fuel, active power losses network, and the system’s loadability index have been used; therefore, through an artificial neural network, the comparison of two propagation algorithms of this type of network, and the definition of the model, OPF analysis has been and define carried out. The performance of these two algorithms is analyzed and compared using the model assessment index and MGN tests the statistical method of Bootstrap has been used to achieve the best performance in order to improve OPF estimates. The Bayesian and Perceptron neural networks have been studied in IEEE 30 bus test system to reduce the steps with less than 1%, and to improve the OPF estimation with single-objective optimization functions .The results show the effective role of bootstrapped Bayesian neural network in terms of performance by using MATLAB software.
Type of Study: Research |
Subject:
Electrical Engineering
Received: 2016/07/24 | Revised: 2020/01/14 | Accepted: 2018/10/27 | Published: 2019/10/2
Received: 2016/07/24 | Revised: 2020/01/14 | Accepted: 2018/10/27 | Published: 2019/10/2
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