A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer
Phase-shifting transformer (PST) is one of the flexible AC transmission technologies to solve the problem of uneven power transmission. Considering that PST can also be used as a regulation means for the economic operation of the system, it is necessary to study the power flow optimization of power...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-01-01
|
| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.793686/full |
| _version_ | 1819279209489498112 |
|---|---|
| author | Zuohong Li Feng Li Ruoping Liu Mengze Yu Zhiying Chen Zihao Xie Zhaobin Du |
| author_facet | Zuohong Li Feng Li Ruoping Liu Mengze Yu Zhiying Chen Zihao Xie Zhaobin Du |
| author_sort | Zuohong Li |
| collection | DOAJ |
| description | Phase-shifting transformer (PST) is one of the flexible AC transmission technologies to solve the problem of uneven power transmission. Considering that PST can also be used as a regulation means for the economic operation of the system, it is necessary to study the power flow optimization of power systems with PST. In order to find a more efficient power flow optimization method, an improved genetic algorithm including a data-driven module is proposed. This method uses the deep belief network (DBN) to train the sample set of the power flow and obtains a high-precision proxy model. Then, the calculation of the DBN model replaces the traditional adaptation function calculation link which is very time-consuming due to a great quantity of AC power flow solution work. In addition, the sectional power flow reversal elimination mechanism in the genetic algorithm is introduced and appropriately co-designed with DBN to avoid an unreasonable power flow distribution of the grid section with PST. Finally, by comparing with the traditional model-driven genetic algorithm and traditional mathematical programming method, the feasibility and the validity of the method proposed in this paper are verified on the IEEE 39-node system. |
| first_indexed | 2024-12-24T00:24:16Z |
| format | Article |
| id | doaj.art-b0e1a4994a8f49f995cb33a702abafff |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-24T00:24:16Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-b0e1a4994a8f49f995cb33a702abafff2022-12-21T17:24:31ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2022-01-01910.3389/fenrg.2021.793686793686A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting TransformerZuohong Li0Feng Li1Ruoping Liu2Mengze Yu3Zhiying Chen4Zihao Xie5Zhaobin Du6The Grid Planning and Research Center of Guangdong Power Grid Corporation, Guangzhou, ChinaThe Grid Planning and Research Center of Guangdong Power Grid Corporation, Guangzhou, ChinaThe Grid Planning and Research Center of Guangdong Power Grid Corporation, Guangzhou, ChinaThe Grid Planning and Research Center of Guangdong Power Grid Corporation, 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, ChinaPhase-shifting transformer (PST) is one of the flexible AC transmission technologies to solve the problem of uneven power transmission. Considering that PST can also be used as a regulation means for the economic operation of the system, it is necessary to study the power flow optimization of power systems with PST. In order to find a more efficient power flow optimization method, an improved genetic algorithm including a data-driven module is proposed. This method uses the deep belief network (DBN) to train the sample set of the power flow and obtains a high-precision proxy model. Then, the calculation of the DBN model replaces the traditional adaptation function calculation link which is very time-consuming due to a great quantity of AC power flow solution work. In addition, the sectional power flow reversal elimination mechanism in the genetic algorithm is introduced and appropriately co-designed with DBN to avoid an unreasonable power flow distribution of the grid section with PST. Finally, by comparing with the traditional model-driven genetic algorithm and traditional mathematical programming method, the feasibility and the validity of the method proposed in this paper are verified on the IEEE 39-node system.https://www.frontiersin.org/articles/10.3389/fenrg.2021.793686/fullphase-shifting transformerpower flow optimizationgenetic algorithmdata-drivendeep belief network |
| spellingShingle | Zuohong Li Feng Li Ruoping Liu Mengze Yu Zhiying Chen Zihao Xie Zhaobin Du A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer Frontiers in Energy Research phase-shifting transformer power flow optimization genetic algorithm data-driven deep belief network |
| title | A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer |
| title_full | A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer |
| title_fullStr | A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer |
| title_full_unstemmed | A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer |
| title_short | A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer |
| title_sort | data driven genetic algorithm for power flow optimization in the power system with phase shifting transformer |
| topic | phase-shifting transformer power flow optimization genetic algorithm data-driven deep belief network |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2021.793686/full |
| work_keys_str_mv | AT zuohongli adatadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT fengli adatadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT ruopingliu adatadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT mengzeyu adatadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT zhiyingchen adatadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT zihaoxie adatadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT zhaobindu adatadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT zuohongli datadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT fengli datadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT ruopingliu datadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT mengzeyu datadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT zhiyingchen datadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT zihaoxie datadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer AT zhaobindu datadrivengeneticalgorithmforpowerflowoptimizationinthepowersystemwithphaseshiftingtransformer |