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Research on multi-protocol wireless data analysis and prediction of converter station based on improved particle swarm algorithm
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Science & Technology Review | 2024, 42(2) : 120 - 128
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Science & Technology Review | 2024, 42(2): 120-128
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Research on multi-protocol wireless data analysis and prediction of converter station based on improved particle swarm algorithm
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MAO Chunxiang, CHAI Bin, LIU Ruopeng
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    Ultra-High Voltage Company of State Grid Ningxia Electric Power Co., Ltd, Yinchuan 750000, China
Published: 2024-01-28 doi: 10.3981/j.issn.1000-7857.2024.02.012
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At present, DC transmission is developing toward high voltage and large capacity technology. Its advantages in longdistance transmission, cross-region networking and flexible dispatch are becoming more and more obvious, but at the same time, abnormal outage of DC system caused by critical equipment failure of converter station has a greater impact on power system. Therefore, it is of great significance to enhance the perception of critical DC equipment, to predict and handle the faults of critical DC equipment in advance, to reduce abnormal outage of DC system and to improve power supply reliability. Taking the multi-protocol wireless data of Ningxia State Grid converter station as an example, an improved particle swarm algorithm based on the gray wolf algorithm is proposed. The experimental results show that the particle swarm-wolf algorithm can more accurately predict the monitoring data of the converter station, reduce the prediction error, and provide a basis for the operation and maintenance of the converter station in the future.
converter station  /  wireless data  /  particle swarm algorithm  /  gray wolf algorithm
MAO Chunxiang, CHAI Bin, LIU Ruopeng. Research on multi-protocol wireless data analysis and prediction of converter station based on improved particle swarm algorithm[J]. Science & Technology Review, 2024 , 42 (2) : 120 -128 . DOI: 10.3981/j.issn.1000-7857.2024.02.012
Year 2024 volume 42 Issue 2
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doi: 10.3981/j.issn.1000-7857.2024.02.012
  • Receive Date:2022-06-11
  • Online Date:2024-04-15
  • Published:2024-01-28
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  • Received:2022-06-11
  • Revised:2022-07-27
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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