Volume 18, Issue 4 (December 2022)                   IJEEE 2022, 18(4): 2429-2429 | Back to browse issues page


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Najjarpour M, Tousi B, Jamali S. Loss Reduction in Distribution Networks With DG Units by Correlating Taguchi Method and Genetic Algorithm. IJEEE 2022; 18 (4) :2429-2429
URL: http://ijeee.iust.ac.ir/article-1-2429-en.html
Abstract:   (872 Views)
Optimal power flow is an essential tool in the study of power systems. Distributed generation sources increase network uncertainties due to their random behavior, so the optimal power flow is no longer responsive and the probabilistic optimal power flow must be used. This paper presents a probabilistic optimal power flow algorithm using the Taguchi method based on orthogonal arrays and genetic algorithms. This method can apply correlations and is validated by simulation experiments in the IEEE 30-bus network. The test results of this method are compared with the Monte Carlo simulation results and the two-point estimation method. The purpose of this paper is to reduce the losses of the entire IEEE 30-bus network. The accuracy and efficiency of the proposed Taguchi correlation method and the genetic algorithm are confirmed by comparison with the Monte Carlo simulation and the two-point estimation method. Finally, with this method, we see a reduction of 5.5 MW of losses.
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  1. This paper presents a probabilistic optimal power flow algorithm using the Taguchi method based on orthogonal arrays and genetic algorithms.
  2. This method can apply correlations and is validated by simulation experiments in the IEEE 30-bus network.
  3. The test results of this method are compared with the Monte Carlo simulation results and the two-point estimation method.
  4. The accuracy and efficiency of the proposed Taguchi correlation method and the genetic algorithm are confirmed by comparison with the Monte Carlo simulation and the two-point estimation method.

Type of Study: Research Paper | Subject: Optimal Power Flow
Received: 2022/02/15 | Revised: 2023/01/03 | Accepted: 2022/05/26

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.