Reliability growth model of quantum direct current electricity meter software based on optimization network
Quantum direct current electricity meter is one of the important instruments in smart grid, the reliability growth model is of great significance to improve its reliability. In the past, when several types of commonly-used neural networks were used for modeling, there were problems like low paramete...
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| Format: | Article |
| Language: | zho |
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Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-03-01
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| Series: | Diance yu yibiao |
| Subjects: | |
| Online Access: | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20240711001&flag=1&journal_id=dcyyb&year_id=2025 |
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| author | TIAN Teng QIU Rujia ZHAO Long GENG Jiaqi WANG Enhui SUN Yu |
| author_facet | TIAN Teng QIU Rujia ZHAO Long GENG Jiaqi WANG Enhui SUN Yu |
| author_sort | TIAN Teng |
| collection | DOAJ |
| description | Quantum direct current electricity meter is one of the important instruments in smart grid, the reliability growth model is of great significance to improve its reliability. In the past, when several types of commonly-used neural networks were used for modeling, there were problems like low parameter training efficiency and low generalization ability caused by unsatisfactory parameters, which reduced the prediction accuracy of the models to a certain extent. In this paper, we will replace the training process of the neural network with a parameter optimization process, and use the improved whole annealing genetic algorithm (WAGA) to optimize the parameters of the back propagation neural network. This improves the modeling efficiency by 18 times and significantly improves global optimization ability of the back propagation neural network. Then, the software reliability growth model of WAGA-BPNN is presented, and the experimental data of the software reliability improvement process of quantum DC electricity meter is modeled and verified. Experiments show that the prediction accuracy of the model doubles and meets the practical requirements. |
| format | Article |
| id | doaj-art-fe7bc44729e3430ea29d7b461a508ac3 |
| institution | Kabale University |
| issn | 1001-1390 |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd. |
| record_format | Article |
| series | Diance yu yibiao |
| spelling | doaj-art-fe7bc44729e3430ea29d7b461a508ac32025-08-20T03:42:43ZzhoHarbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.Diance yu yibiao1001-13902025-03-0162321722410.19753/j.issn1001-1390.2025.03.0261001-1390(2025)03-0217-08Reliability growth model of quantum direct current electricity meter software based on optimization networkTIAN Teng0QIU Rujia1ZHAO Long2GENG Jiaqi3WANG Enhui4SUN Yu5State Grid Anhui Electric Power Research Institute, Hefei 230000, ChinaState Grid Anhui Electric Power Research Institute, Hefei 230000, ChinaState Grid Anhui Electric Power Research Institute, Hefei 230000, ChinaState Grid Anhui Electric Power Research Institute, Hefei 230000, ChinaState Grid Anhui Electric Power Research Institute, Hefei 230000, ChinaHeilongjiang Electrical Instrumentation Engineering Technology Research Center Co., Ltd., Harbin 150028, ChinaQuantum direct current electricity meter is one of the important instruments in smart grid, the reliability growth model is of great significance to improve its reliability. In the past, when several types of commonly-used neural networks were used for modeling, there were problems like low parameter training efficiency and low generalization ability caused by unsatisfactory parameters, which reduced the prediction accuracy of the models to a certain extent. In this paper, we will replace the training process of the neural network with a parameter optimization process, and use the improved whole annealing genetic algorithm (WAGA) to optimize the parameters of the back propagation neural network. This improves the modeling efficiency by 18 times and significantly improves global optimization ability of the back propagation neural network. Then, the software reliability growth model of WAGA-BPNN is presented, and the experimental data of the software reliability improvement process of quantum DC electricity meter is modeled and verified. Experiments show that the prediction accuracy of the model doubles and meets the practical requirements.http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20240711001&flag=1&journal_id=dcyyb&year_id=2025reliability growth modelwhole annealing genetic algorithmquantum direct current electricity meter |
| spellingShingle | TIAN Teng QIU Rujia ZHAO Long GENG Jiaqi WANG Enhui SUN Yu Reliability growth model of quantum direct current electricity meter software based on optimization network Diance yu yibiao reliability growth model whole annealing genetic algorithm quantum direct current electricity meter |
| title | Reliability growth model of quantum direct current electricity meter software based on optimization network |
| title_full | Reliability growth model of quantum direct current electricity meter software based on optimization network |
| title_fullStr | Reliability growth model of quantum direct current electricity meter software based on optimization network |
| title_full_unstemmed | Reliability growth model of quantum direct current electricity meter software based on optimization network |
| title_short | Reliability growth model of quantum direct current electricity meter software based on optimization network |
| title_sort | reliability growth model of quantum direct current electricity meter software based on optimization network |
| topic | reliability growth model whole annealing genetic algorithm quantum direct current electricity meter |
| url | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20240711001&flag=1&journal_id=dcyyb&year_id=2025 |
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