Optimal sensor placement in distribution network based on super resolution network
With the development of smart grids and emerging measurement technologies, the massive growth of distribution grid data may impact the reliable, economic, and safe operation of the distribution network. For a large-scale distribution network state estimation, the strategy of measuring data for a dis...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-04-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484721012269 |
| _version_ | 1818272617852829696 |
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| author | Qifeng Lv Ying Chen Keyu Yuan Liwen Qin Xiaoyong Yu Haitao Gui |
| author_facet | Qifeng Lv Ying Chen Keyu Yuan Liwen Qin Xiaoyong Yu Haitao Gui |
| author_sort | Qifeng Lv |
| collection | DOAJ |
| description | With the development of smart grids and emerging measurement technologies, the massive growth of distribution grid data may impact the reliable, economic, and safe operation of the distribution network. For a large-scale distribution network state estimation, the strategy of measuring data for a distribution grid is critical to its economy and reliability. This paper proposed a distribution network state estimation model based on a graph convolutional neural network. The proposed algorithm uses a genetic algorithm to optimize the sensor locations, frequency of sensors in the distribution network and configures of devices to guarantee the proposed state estimation data accuracy. With minimizing costs of investment and operation, the proposed graph convolutional neural network provides super-resolution state estimation data of the distribution network by using low-resolution measurement data. The proposed method is tested and verified by the IEEE33 distribution network system and the testing result demonstrates the feasibility and effectiveness of the proposed model and algorithm. |
| first_indexed | 2024-12-12T21:44:55Z |
| format | Article |
| id | doaj.art-cdfc9bca40b24d3f899a88441feefcc6 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-12-12T21:44:55Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-cdfc9bca40b24d3f899a88441feefcc62022-12-22T00:10:57ZengElsevierEnergy Reports2352-48472022-04-01810501058Optimal sensor placement in distribution network based on super resolution networkQifeng Lv0Ying Chen1Keyu Yuan2Liwen Qin3Xiaoyong Yu4Haitao Gui5Tsinghua University, Shuangqin Road 30#, Beijing, 100084, ChinaTsinghua University, Shuangqin Road 30#, Beijing, 100084, China; Corresponding author.Tsinghua University, Shuangqin Road 30#, Beijing, 100084, ChinaElectric Power Research Institute of Guangxi Power Grid Co., Ltd., Mingzhu Road 6-2#, Nanning, 530023, ChinaElectric Power Research Institute of Guangxi Power Grid Co., Ltd., Mingzhu Road 6-2#, Nanning, 530023, ChinaGuilin Power Supply Bureau of Guangxi Power Grid Co., Ltd., Shanghai Road 15#, Guilin, 541002, ChinaWith the development of smart grids and emerging measurement technologies, the massive growth of distribution grid data may impact the reliable, economic, and safe operation of the distribution network. For a large-scale distribution network state estimation, the strategy of measuring data for a distribution grid is critical to its economy and reliability. This paper proposed a distribution network state estimation model based on a graph convolutional neural network. The proposed algorithm uses a genetic algorithm to optimize the sensor locations, frequency of sensors in the distribution network and configures of devices to guarantee the proposed state estimation data accuracy. With minimizing costs of investment and operation, the proposed graph convolutional neural network provides super-resolution state estimation data of the distribution network by using low-resolution measurement data. The proposed method is tested and verified by the IEEE33 distribution network system and the testing result demonstrates the feasibility and effectiveness of the proposed model and algorithm.http://www.sciencedirect.com/science/article/pii/S2352484721012269Distribution networkSensorGraph convolution neural networkGenetic algorithmTemporal resolutionSuper-resolution |
| spellingShingle | Qifeng Lv Ying Chen Keyu Yuan Liwen Qin Xiaoyong Yu Haitao Gui Optimal sensor placement in distribution network based on super resolution network Energy Reports Distribution network Sensor Graph convolution neural network Genetic algorithm Temporal resolution Super-resolution |
| title | Optimal sensor placement in distribution network based on super resolution network |
| title_full | Optimal sensor placement in distribution network based on super resolution network |
| title_fullStr | Optimal sensor placement in distribution network based on super resolution network |
| title_full_unstemmed | Optimal sensor placement in distribution network based on super resolution network |
| title_short | Optimal sensor placement in distribution network based on super resolution network |
| title_sort | optimal sensor placement in distribution network based on super resolution network |
| topic | Distribution network Sensor Graph convolution neural network Genetic algorithm Temporal resolution Super-resolution |
| url | http://www.sciencedirect.com/science/article/pii/S2352484721012269 |
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