Volume 16, Issue 3 (September 2020)                   IJEEE 2020, 16(3): 292-301 | Back to browse issues page

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Hassannejad Marzouni A, Zakariazadeh A. Error Modeling in Distribution Network State Estimation Using RBF-Based Artificial Neural Network. IJEEE 2020; 16 (3) :292-301
URL: http://ijeee.iust.ac.ir/article-1-1482-en.html
Error Modeling in Distribution Network State Estimation Using RBF-Based Artificial Neural Network
A. Hassannejad Marzouni , A. Zakariazadeh
Abstract:   (3491 Views)
State estimation is essential to access observable network models for online monitoring and analyzing of power systems. Due to the integration of distributed energy resources and new technologies, state estimation in distribution systems would be necessary. However, accurate input data are essential for an accurate estimation along with knowledge on the possible correlation between the real and pseudo measurements data. This study presents a new approach to model errors for the distribution system state estimation purpose. In this paper, pseudo measurements are generated using a couple of real measurements data by means of the artificial neural network method. In the proposed method, the radial basis function network with the Gaussian kernel is also implemented to decompose pseudo measurements into several components. The robustness of the proposed error modeling method is assessed on IEEE 123-bus distribution test system where the problem is optimized by the imperialist competitive algorithm. The results evidence that the proposed method causes to increase in detachment accuracy of error components which results in presenting higher quality output in the distribution state estimation.
Keywords: State Estimation , Distribution Network , Error Modeling , ANN , Radial Basis Function
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  • To model pseudo measurements using multilayer perceptron artificial neural network
  • To implement a radial basis function neural network in error modeling.
  • To optimize the RBF neural network by the imperialist competitive algorithm.

Type of Study: Research Paper |
Received: 2019/04/27 | Revised: 2019/11/25 | Accepted: 2019/11/29
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 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.