LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks
The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the tradition...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/8867190 |
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| _version_ | 1832560132003725312 |
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| author | Yunliang Chen Shaoqian Chen Nian Zhang Hao Liu Honglei Jing Geyong Min |
| author_facet | Yunliang Chen Shaoqian Chen Nian Zhang Hao Liu Honglei Jing Geyong Min |
| author_sort | Yunliang Chen |
| collection | DOAJ |
| description | The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict the health status of the power grid sensor network. The LPR-MLP model consists of two parts: (1) local binary pattern (LBP), principal component analysis (PCA), and ReliefF are used to process image data and meteorological and mechanical data and (2) the multilayer perceptron (MLP) method is then applied to build the prediction model. The results obtained from extensive experiments on the real-world data collected from the online system of China Southern Power Grid demonstrate that this new LPR-MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods. |
| format | Article |
| id | doaj-art-ab7da1ea3fdf4583ae317103f5031b51 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ab7da1ea3fdf4583ae317103f5031b512025-02-03T01:28:23ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/88671908867190LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor NetworksYunliang Chen0Shaoqian Chen1Nian Zhang2Hao Liu3Honglei Jing4Geyong Min5School of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaCollege of Engineering, Mathematics, and Physical Sciences, University of Exeter, EX4 4QF, Exeter, UKThe safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict the health status of the power grid sensor network. The LPR-MLP model consists of two parts: (1) local binary pattern (LBP), principal component analysis (PCA), and ReliefF are used to process image data and meteorological and mechanical data and (2) the multilayer perceptron (MLP) method is then applied to build the prediction model. The results obtained from extensive experiments on the real-world data collected from the online system of China Southern Power Grid demonstrate that this new LPR-MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods.http://dx.doi.org/10.1155/2021/8867190 |
| spellingShingle | Yunliang Chen Shaoqian Chen Nian Zhang Hao Liu Honglei Jing Geyong Min LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks Complexity |
| title | LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks |
| title_full | LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks |
| title_fullStr | LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks |
| title_full_unstemmed | LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks |
| title_short | LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks |
| title_sort | lpr mlp a novel health prediction model for transmission lines in grid sensor networks |
| url | http://dx.doi.org/10.1155/2021/8867190 |
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