Adaptive Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network
Accurate device parameters play a critical role in the calculation and analysis of power distribution networks (PDNs). However, device parameters are always affected by the operating status and influenced by manual entry. Besides, the distribution area of PDN is very wide, which brings more challeng...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/9300522 |
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| _version_ | 1832562393700368384 |
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| author | Bin Li Yehai Jiang Ke Hu Xiangyi Zhou Haoran Chen Shihe Xu Hao Jiao Jinming Chen |
| author_facet | Bin Li Yehai Jiang Ke Hu Xiangyi Zhou Haoran Chen Shihe Xu Hao Jiao Jinming Chen |
| author_sort | Bin Li |
| collection | DOAJ |
| description | Accurate device parameters play a critical role in the calculation and analysis of power distribution networks (PDNs). However, device parameters are always affected by the operating status and influenced by manual entry. Besides, the distribution area of PDN is very wide, which brings more challenges to parameter identification work. Therefore, developing appropriate algorithms for accurately identifying PDN parameters has attracted much more attention from researchers recently. Most of the existing parameter identification algorithms are gradient-free and based on heuristic schemes. Herein, an adaptive gradient-based method is proposed for parameter identification in PDN. The analytical expressions of the gradients of the loss function with respect to the parameters are derived, and an adaptive updating scheme is utilized. By comparing the proposed method and several heuristic algorithms, it is found that the errors in both three criteria via our solution are much lower with a much smoother and more stable convergence of loss function. By further taking a linear transformation of the loss function, the method of this work significantly promotes the parameter identification performance with much lower variance in repeat experiments, indicating that the proposed method in this work achieves a more robust performance to identify PDN parameters. This work gives a practical demonstration by utilizing the gradient-based method for parameter identification of PDN. |
| format | Article |
| id | doaj-art-33cc07908b51408b821f5c8b1654ae85 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-33cc07908b51408b821f5c8b1654ae852025-02-03T01:22:40ZengWileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/9300522Adaptive Gradient-Based Optimization Method for Parameter Identification in Power Distribution NetworkBin Li0Yehai Jiang1Ke Hu2Xiangyi Zhou3Haoran Chen4Shihe Xu5Hao Jiao6Jinming Chen7Nanjing Institute of TechnologyNanjing Institute of TechnologyChongqing University of Posts and TelecommunicationsChongqing UniversityNational University of Defense TechnologyUniversity of Science and Technology of ChinaState Grid Jiangsu Electric Power Co., Ltd., Research InstituteState Grid Jiangsu Electric Power Co., Ltd., Research InstituteAccurate device parameters play a critical role in the calculation and analysis of power distribution networks (PDNs). However, device parameters are always affected by the operating status and influenced by manual entry. Besides, the distribution area of PDN is very wide, which brings more challenges to parameter identification work. Therefore, developing appropriate algorithms for accurately identifying PDN parameters has attracted much more attention from researchers recently. Most of the existing parameter identification algorithms are gradient-free and based on heuristic schemes. Herein, an adaptive gradient-based method is proposed for parameter identification in PDN. The analytical expressions of the gradients of the loss function with respect to the parameters are derived, and an adaptive updating scheme is utilized. By comparing the proposed method and several heuristic algorithms, it is found that the errors in both three criteria via our solution are much lower with a much smoother and more stable convergence of loss function. By further taking a linear transformation of the loss function, the method of this work significantly promotes the parameter identification performance with much lower variance in repeat experiments, indicating that the proposed method in this work achieves a more robust performance to identify PDN parameters. This work gives a practical demonstration by utilizing the gradient-based method for parameter identification of PDN.http://dx.doi.org/10.1155/2022/9300522 |
| spellingShingle | Bin Li Yehai Jiang Ke Hu Xiangyi Zhou Haoran Chen Shihe Xu Hao Jiao Jinming Chen Adaptive Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network International Transactions on Electrical Energy Systems |
| title | Adaptive Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network |
| title_full | Adaptive Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network |
| title_fullStr | Adaptive Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network |
| title_full_unstemmed | Adaptive Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network |
| title_short | Adaptive Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network |
| title_sort | adaptive gradient based optimization method for parameter identification in power distribution network |
| url | http://dx.doi.org/10.1155/2022/9300522 |
| work_keys_str_mv | AT binli adaptivegradientbasedoptimizationmethodforparameteridentificationinpowerdistributionnetwork AT yehaijiang adaptivegradientbasedoptimizationmethodforparameteridentificationinpowerdistributionnetwork AT kehu adaptivegradientbasedoptimizationmethodforparameteridentificationinpowerdistributionnetwork AT xiangyizhou adaptivegradientbasedoptimizationmethodforparameteridentificationinpowerdistributionnetwork AT haoranchen adaptivegradientbasedoptimizationmethodforparameteridentificationinpowerdistributionnetwork AT shihexu adaptivegradientbasedoptimizationmethodforparameteridentificationinpowerdistributionnetwork AT haojiao adaptivegradientbasedoptimizationmethodforparameteridentificationinpowerdistributionnetwork AT jinmingchen adaptivegradientbasedoptimizationmethodforparameteridentificationinpowerdistributionnetwork |