Three-phase AC/DC power-flow for balanced/unbalanced microgrids including wind/solar, droop-controlled and electronically-coupled distributed energy resources using radial basis function neural networks
This study presents a novel approach for robust, balanced and unbalanced power-flow analysis of microgrids including wind/solar, droop-controlled and electronically-coupled distributed energy resources. This method is based on using radial basis function neural networks that can be applied to a wide...
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| Published in: | IET power electronics Vol. 10; no. 3; pp. 313 - 328 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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The Institution of Engineering and Technology
10.03.2017
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| Subjects: | |
| ISSN: | 1755-4535, 1755-4543 |
| Online Access: | Get full text |
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| Abstract | This study presents a novel approach for robust, balanced and unbalanced power-flow analysis of microgrids including wind/solar, droop-controlled and electronically-coupled distributed energy resources. This method is based on using radial basis function neural networks that can be applied to a wide range of non-linear equation sets. Unlike conventional Newton–Raphson, the presented method does not need to calculate partial derivatives and inverse Jacobian matrix and so, has less computation time, can solve all the equation sets for the power grid and distributed energy resources exactly and simultaneously, and has enough robustness with respect to the R/X ratio and load changes. Also, because the power electronic interface provides some degrees of freedom in the steady-state and dynamic models, a new approach is required to solve the non-linear set of the power grid and distributed energy resource equations even with unequal number of equations and variables. The proposed method is a general method applicable to all types of power networks, including radial, meshed, and open-loop, and includes all types of buses, i.e. PQ, photovoltaic and slack buses. This method is tested on different microgrid test systems, and the comparative results validate its efficiency and accuracy. |
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| AbstractList | This study presents a novel approach for robust, balanced and unbalanced power-flow analysis of microgrids including wind/solar, droop-controlled and electronically-coupled distributed energy resources. This method is based on using radial basis function neural networks that can be applied to a wide range of non-linear equation sets. Unlike conventional Newton-Raphson, the presented method does not need to calculate partial derivatives and inverse Jacobian matrix and so, has less computation time, can solve all the equation sets for the power grid and distributed energy resources exactly and simultaneously, and has enough robustness with respect to the R/X ratio and load changes. Also, because the power electronic interface provides some degrees of freedom in the steady-state and dynamic models, a new approach is required to solve the non-linear set of the power grid and distributed energy resource equations even with unequal number of equations and variables. The proposed method is a general method applicable to all types of power networks, including radial, meshed, and open-loop, and includes all types of buses, i.e. PQ, photovoltaic and slack buses. This method is tested on different microgrid test systems, and the comparative results validate its efficiency and accuracy. |
| Author | Gharehpetian, Gevork B Baghaee, Hamid Reza Mirsalim, Mojtaba Talebi, Heidar Ali |
| Author_xml | – sequence: 1 givenname: Hamid Reza surname: Baghaee fullname: Baghaee, Hamid Reza organization: Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran – sequence: 2 givenname: Mojtaba surname: Mirsalim fullname: Mirsalim, Mojtaba email: mirsalim@aut.ac.ir organization: Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran – sequence: 3 givenname: Gevork B surname: Gharehpetian fullname: Gharehpetian, Gevork B organization: Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran – sequence: 4 givenname: Heidar Ali surname: Talebi fullname: Talebi, Heidar Ali organization: Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran |
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| Copyright | The Institution of Engineering and Technology 2021 The Authors. IET Power Electronics published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology |
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| Keywords | non-linear equation sets bus radial basis function neural network steady-state model distributed power generation power grid busbars radial basis function networks power grids dynamic model solar power balanced microgrid wind-solar droop-controlled distributed energy resource three-phase AC-DC power flow power engineering computing microgrid test system R/X ratio nonlinear equations power network wind power distributed energy resource equation unbalanced microgrid power electronic interface load flow wind-solar electronically-coupled distributed energy resource |
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| SubjectTerms | balanced microgrid bus busbars distributed energy resource equation Distributed generation distributed power generation dynamic model Electric power grids Electronics load flow Mathematical analysis Mathematical models microgrid test system Neural networks nonlinear equations non‐linear equation sets power electronic interface power engineering computing power grid power grids power network R/X ratio Radial basis function radial basis function networks radial basis function neural network solar power steady‐state model three‐phase AC‐DC power flow unbalanced microgrid wind power Wind power generation wind‐solar droop‐controlled distributed energy resource wind‐solar electronically‐coupled distributed energy resource |
| Title | Three-phase AC/DC power-flow for balanced/unbalanced microgrids including wind/solar, droop-controlled and electronically-coupled distributed energy resources using radial basis function neural networks |
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