Protection Algorithm Based on Two-Dimensional Spatial Current Trajectory Image and Deep Learning for Transmission Lines Connecting Photovoltaic Stations
Fiber differential protection (FDP) is the primary protection scheme in power systems. However, with the increasing proportion of photovoltaic (PV) grids connected in the power system, the controllability and weak power supply characteristics of photovoltaic power stations change the amplitude and p...
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6066 |
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| author | Panrun Jin Jianling Liao Wenqin Song Xushan Zhao Yankui Zhang |
| author_facet | Panrun Jin Jianling Liao Wenqin Song Xushan Zhao Yankui Zhang |
| author_sort | Panrun Jin |
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| description | Fiber differential protection (FDP) is the primary protection scheme in power systems. However, with the increasing proportion of photovoltaic (PV) grids connected in the power system, the controllability and weak power supply characteristics of photovoltaic power stations change the amplitude and phase angle characteristics of fault currents, which makes the sensitivity of fiber differential protection decline and even increases the risk of failure to operate. In view of this phenomenon, combined with the digital and intelligent development of the new energy power system, this study integrates deep learning with relay protection to propose a protection algorithm based on a two-dimensional spatial current trajectory image and deep learning. In this algorithm, the PV side current and the system side current are, respectively, mapped to the two-dimensional space plane as X- and Y-axes to form the current trajectory image. Under different fault conditions, they have obvious differences. A data-enhanced convolutional neural network (A-CNN) based on cross-overlapping data sets is used to identify trajectory features and locate faults. After performance evaluation, the protection algorithm has the advantages of adapting to new energy access, resisting transition resistance, and robustness to current transformer (CT) saturation, and outliers. |
| format | Article |
| id | doaj-art-0fdbcd16efa54015a88e5a481f978a55 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0fdbcd16efa54015a88e5a481f978a552025-08-20T02:33:01ZengMDPI AGApplied Sciences2076-34172025-05-011511606610.3390/app15116066Protection Algorithm Based on Two-Dimensional Spatial Current Trajectory Image and Deep Learning for Transmission Lines Connecting Photovoltaic StationsPanrun Jin0Jianling Liao1Wenqin Song2Xushan Zhao3Yankui Zhang4Economic and Technical Research Institute of Gansu Electric Power Company, Lanzhou 730050, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining & Technology-Beijing, Beijing 100083, ChinaEconomic and Technical Research Institute of Gansu Electric Power Company, Lanzhou 730050, ChinaEconomic and Technical Research Institute of Gansu Electric Power Company, Lanzhou 730050, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining & Technology-Beijing, Beijing 100083, ChinaFiber differential protection (FDP) is the primary protection scheme in power systems. However, with the increasing proportion of photovoltaic (PV) grids connected in the power system, the controllability and weak power supply characteristics of photovoltaic power stations change the amplitude and phase angle characteristics of fault currents, which makes the sensitivity of fiber differential protection decline and even increases the risk of failure to operate. In view of this phenomenon, combined with the digital and intelligent development of the new energy power system, this study integrates deep learning with relay protection to propose a protection algorithm based on a two-dimensional spatial current trajectory image and deep learning. In this algorithm, the PV side current and the system side current are, respectively, mapped to the two-dimensional space plane as X- and Y-axes to form the current trajectory image. Under different fault conditions, they have obvious differences. A data-enhanced convolutional neural network (A-CNN) based on cross-overlapping data sets is used to identify trajectory features and locate faults. After performance evaluation, the protection algorithm has the advantages of adapting to new energy access, resisting transition resistance, and robustness to current transformer (CT) saturation, and outliers.https://www.mdpi.com/2076-3417/15/11/6066convolutional neural networkcurrent trajectoryCT saturationfault location |
| spellingShingle | Panrun Jin Jianling Liao Wenqin Song Xushan Zhao Yankui Zhang Protection Algorithm Based on Two-Dimensional Spatial Current Trajectory Image and Deep Learning for Transmission Lines Connecting Photovoltaic Stations Applied Sciences convolutional neural network current trajectory CT saturation fault location |
| title | Protection Algorithm Based on Two-Dimensional Spatial Current Trajectory Image and Deep Learning for Transmission Lines Connecting Photovoltaic Stations |
| title_full | Protection Algorithm Based on Two-Dimensional Spatial Current Trajectory Image and Deep Learning for Transmission Lines Connecting Photovoltaic Stations |
| title_fullStr | Protection Algorithm Based on Two-Dimensional Spatial Current Trajectory Image and Deep Learning for Transmission Lines Connecting Photovoltaic Stations |
| title_full_unstemmed | Protection Algorithm Based on Two-Dimensional Spatial Current Trajectory Image and Deep Learning for Transmission Lines Connecting Photovoltaic Stations |
| title_short | Protection Algorithm Based on Two-Dimensional Spatial Current Trajectory Image and Deep Learning for Transmission Lines Connecting Photovoltaic Stations |
| title_sort | protection algorithm based on two dimensional spatial current trajectory image and deep learning for transmission lines connecting photovoltaic stations |
| topic | convolutional neural network current trajectory CT saturation fault location |
| url | https://www.mdpi.com/2076-3417/15/11/6066 |
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