Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework

Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework

Electric loads are essential for power system dynamic simulation. However, load modeling is one of the most challenging topics due to the diversity and time-varying behavior of the load. When considering the intervention of rapidly developing distributed generation (DG), load modeling becomes more d...

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Bibliographic Details
Main Authors: Jianquan Zhu, Tianyun Luo, Jiajun Chen, Yunrui Xia, Chenxi Wang, Mingbo Liu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Load modeling
distributed generation
multi-model
RBF neural network
Bayesian estimation
diversity
Online Access:https://ieeexplore.ieee.org/document/8817918/
id doaj-2532bdd625a34f46a97faf75fd8582c7
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spelling doaj-2532bdd625a34f46a97faf75fd8582c72021-03-29T23:24:42ZengIEEEIEEE Access2169-35362019-01-01712114512115510.1109/ACCESS.2019.29380998817918Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model FrameworkJianquan Zhu0https://orcid.org/0000-0001-6701-4018Tianyun Luo1Jiajun Chen2Yunrui Xia3Chenxi Wang4Mingbo Liu5https://orcid.org/0000-0001-9097-9045School of Electric Power Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou, ChinaElectric loads are essential for power system dynamic simulation. However, load modeling is one of the most challenging topics due to the diversity and time-varying behavior of the load. When considering the intervention of rapidly developing distributed generation (DG), load modeling becomes more difficult. In this paper, a new solution for determining the unknown generalized load model is proposed. The radial basis function (RBF) neural network-based sub-models of generalized load are stored in the form of a sub-model bank. A recursive Bayesian approach is used to identify the sub-models and then merge them into one generalized load model according to their probabilities. The proposed method can be implemented online and adapt to describing the diversity and time-varying behavior of the generalized load. Numerical studies are carried out using both simulation data and actual measurements. The comparisons with other load modeling methods verify the advantages of the proposed method.https://ieeexplore.ieee.org/document/8817918/Load modelingdistributed generationmulti-modelRBF neural networkBayesian estimationdiversity
collection DOAJ
language English
format Article
sources DOAJ
author Jianquan Zhu
Tianyun Luo
Jiajun Chen
Yunrui Xia
Chenxi Wang
Mingbo Liu
spellingShingle Jianquan Zhu
Tianyun Luo
Jiajun Chen
Yunrui Xia
Chenxi Wang
Mingbo Liu
Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework
IEEE Access
Load modeling
distributed generation
multi-model
RBF neural network
Bayesian estimation
diversity
author_facet Jianquan Zhu
Tianyun Luo
Jiajun Chen
Yunrui Xia
Chenxi Wang
Mingbo Liu
author_sort Jianquan Zhu
title Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework
title_short Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework
title_full Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework
title_fullStr Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework
title_full_unstemmed Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework
title_sort recursive bayesian-based approach for online automatic identification of generalized electric load models in a multi-model framework
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description Electric loads are essential for power system dynamic simulation. However, load modeling is one of the most challenging topics due to the diversity and time-varying behavior of the load. When considering the intervention of rapidly developing distributed generation (DG), load modeling becomes more difficult. In this paper, a new solution for determining the unknown generalized load model is proposed. The radial basis function (RBF) neural network-based sub-models of generalized load are stored in the form of a sub-model bank. A recursive Bayesian approach is used to identify the sub-models and then merge them into one generalized load model according to their probabilities. The proposed method can be implemented online and adapt to describing the diversity and time-varying behavior of the generalized load. Numerical studies are carried out using both simulation data and actual measurements. The comparisons with other load modeling methods verify the advantages of the proposed method.
topic Load modeling
distributed generation
multi-model
RBF neural network
Bayesian estimation
diversity
url https://ieeexplore.ieee.org/document/8817918/
work_keys_str_mv AT jianquanzhu recursivebayesianbasedapproachforonlineautomaticidentificationofgeneralizedelectricloadmodelsinamultimodelframework
AT tianyunluo recursivebayesianbasedapproachforonlineautomaticidentificationofgeneralizedelectricloadmodelsinamultimodelframework
AT jiajunchen recursivebayesianbasedapproachforonlineautomaticidentificationofgeneralizedelectricloadmodelsinamultimodelframework
AT yunruixia recursivebayesianbasedapproachforonlineautomaticidentificationofgeneralizedelectricloadmodelsinamultimodelframework
AT chenxiwang recursivebayesianbasedapproachforonlineautomaticidentificationofgeneralizedelectricloadmodelsinamultimodelframework
AT mingboliu recursivebayesianbasedapproachforonlineautomaticidentificationofgeneralizedelectricloadmodelsinamultimodelframework
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