Power line fault diagnosis based on convolutional neural networks
With the rapid development of the national economy, power security is very important for the security of the country and people's happiness. Electricity is an important energy source for a country. Even if the power system malfunctions for a short period of time, it would cause incalculable los...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-04-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024050527 |
| _version_ | 1827261833702014976 |
|---|---|
| author | Liang Ning Dongfeng Pei |
| author_facet | Liang Ning Dongfeng Pei |
| author_sort | Liang Ning |
| collection | DOAJ |
| description | With the rapid development of the national economy, power security is very important for the security of the country and people's happiness. Electricity is an important energy source for a country. Even if the power system malfunctions for a short period of time, it would cause incalculable losses to social production and people's lives. Among them, one of the most important reasons for power system faults is the occurrence of power line faults, so diagnosing faulty lines has great research significance. On the basis of analyzing the structure and working principle of the deep learning model convolutional neural network (CNN), this article used the CNN model to diagnose faults in power lines and analyzed the simulation results. It was found that different CNN structures have different fault diagnosis accuracy for power lines. The fewer the number of batches in the network structure and the more the number of training sessions, the higher its fault determination accuracy. In the power line fault diagnosis based on three deep learning algorithms, the CNN has the highest stable fault diagnosis accuracy of 100%; the recursive neural network has the second stable fault diagnosis accuracy of 93.4%; the deep belief network has the lowest stable fault diagnosis accuracy of 91.5%. In the comparison of power line fault diagnosis stability, the accuracy standard deviation of CNN is close to 0, and they are also the most stable in power circuit fault diagnosis. The stability of algorithmic recurrent neural networks is between the two, and the accuracy standard deviation of deep belief networks is 1.84% when trained 12 times. Their fault diagnosis stability is also the worst. |
| first_indexed | 2024-04-24T10:57:17Z |
| format | Article |
| id | doaj.art-447329f244234220a84cf584e68890f1 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2025-03-22T03:04:40Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-447329f244234220a84cf584e68890f12024-05-01T05:08:16ZengElsevierHeliyon2405-84402024-04-01108e29021Power line fault diagnosis based on convolutional neural networksLiang Ning0Dongfeng Pei1Tangshan Power Supply Company State Grid Jibei Electric Power Co.Ltd, Tangshan 063000, Hebei, China; Corresponding author.State Grid Hebei Electric Power Liability Company Hada Power, Hada, 056000, Hebei, ChinaWith the rapid development of the national economy, power security is very important for the security of the country and people's happiness. Electricity is an important energy source for a country. Even if the power system malfunctions for a short period of time, it would cause incalculable losses to social production and people's lives. Among them, one of the most important reasons for power system faults is the occurrence of power line faults, so diagnosing faulty lines has great research significance. On the basis of analyzing the structure and working principle of the deep learning model convolutional neural network (CNN), this article used the CNN model to diagnose faults in power lines and analyzed the simulation results. It was found that different CNN structures have different fault diagnosis accuracy for power lines. The fewer the number of batches in the network structure and the more the number of training sessions, the higher its fault determination accuracy. In the power line fault diagnosis based on three deep learning algorithms, the CNN has the highest stable fault diagnosis accuracy of 100%; the recursive neural network has the second stable fault diagnosis accuracy of 93.4%; the deep belief network has the lowest stable fault diagnosis accuracy of 91.5%. In the comparison of power line fault diagnosis stability, the accuracy standard deviation of CNN is close to 0, and they are also the most stable in power circuit fault diagnosis. The stability of algorithmic recurrent neural networks is between the two, and the accuracy standard deviation of deep belief networks is 1.84% when trained 12 times. Their fault diagnosis stability is also the worst.http://www.sciencedirect.com/science/article/pii/S2405844024050527CNNPower linesFault locationFault diagnosis |
| spellingShingle | Liang Ning Dongfeng Pei Power line fault diagnosis based on convolutional neural networks Heliyon CNN Power lines Fault location Fault diagnosis |
| title | Power line fault diagnosis based on convolutional neural networks |
| title_full | Power line fault diagnosis based on convolutional neural networks |
| title_fullStr | Power line fault diagnosis based on convolutional neural networks |
| title_full_unstemmed | Power line fault diagnosis based on convolutional neural networks |
| title_short | Power line fault diagnosis based on convolutional neural networks |
| title_sort | power line fault diagnosis based on convolutional neural networks |
| topic | CNN Power lines Fault location Fault diagnosis |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024050527 |
| work_keys_str_mv | AT liangning powerlinefaultdiagnosisbasedonconvolutionalneuralnetworks AT dongfengpei powerlinefaultdiagnosisbasedonconvolutionalneuralnetworks |