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FUNCTION ANALYSIS OF POWER FAULT DATA INTEGRATED PROCESSING SYSTEM BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGY.

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Τίτλος: FUNCTION ANALYSIS OF POWER FAULT DATA INTEGRATED PROCESSING SYSTEM BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGY.
Συγγραφείς: XIAO, Tongxin, LI, Da, YU, Guoliang, JIN, Zhiyu, WANG, Longshan, JI, Chunxue, ZHAO, Zhongying, FENG, Zejian
Πηγή: Diagnostyka; 2025, Vol. 26 Issue 4, p1-12, 12p
Θεματικοί όροι: Artificial intelligence, Fault diagnosis, Electricity safety, Classification, Electronic data processing, Feature extraction, Electric power systems, Artificial neural networks
Περίληψη: With the increasing scale and complexity of power systems, the rapid and accurate detection of power failures ensures the safe and stable operation of power systems. Traditional fault diagnosis methods rely on manual experience, which has some problems such as slow response and insufficient accuracy. In this study, a comprehensive power fault data processing system based on artificial intelligence technology is proposed. Deep neural network (DNN) model is adopted to classify and detect power fault data, and high-quality data support is provided for model training through data collection, pre-processing, feature extraction and other links. The DNN model has achieved high accuracy in power fault detection, with the classification accuracy reaching 93.4% and fault detection rate 92.0%, and the false positive rate is kept at a low level. It improves the efficiency and accuracy of power fault detection, and provides a reference for the application of artificial intelligence in power system. The research results are of great significance for optimizing the fault handling process of power system and improving power safety. [ABSTRACT FROM AUTHOR]
Copyright of Diagnostyka is the property of Polish Society of Technical Diagnostics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: FUNCTION ANALYSIS OF POWER FAULT DATA INTEGRATED PROCESSING SYSTEM BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGY.
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  Data: <searchLink fieldCode="AR" term="%22XIAO%2C+Tongxin%22">XIAO, Tongxin</searchLink><br /><searchLink fieldCode="AR" term="%22LI%2C+Da%22">LI, Da</searchLink><br /><searchLink fieldCode="AR" term="%22YU%2C+Guoliang%22">YU, Guoliang</searchLink><br /><searchLink fieldCode="AR" term="%22JIN%2C+Zhiyu%22">JIN, Zhiyu</searchLink><br /><searchLink fieldCode="AR" term="%22WANG%2C+Longshan%22">WANG, Longshan</searchLink><br /><searchLink fieldCode="AR" term="%22JI%2C+Chunxue%22">JI, Chunxue</searchLink><br /><searchLink fieldCode="AR" term="%22ZHAO%2C+Zhongying%22">ZHAO, Zhongying</searchLink><br /><searchLink fieldCode="AR" term="%22FENG%2C+Zejian%22">FENG, Zejian</searchLink>
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  Data: Diagnostyka; 2025, Vol. 26 Issue 4, p1-12, 12p
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Electricity+safety%22">Electricity safety</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink>
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  Label: Abstract
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  Data: With the increasing scale and complexity of power systems, the rapid and accurate detection of power failures ensures the safe and stable operation of power systems. Traditional fault diagnosis methods rely on manual experience, which has some problems such as slow response and insufficient accuracy. In this study, a comprehensive power fault data processing system based on artificial intelligence technology is proposed. Deep neural network (DNN) model is adopted to classify and detect power fault data, and high-quality data support is provided for model training through data collection, pre-processing, feature extraction and other links. The DNN model has achieved high accuracy in power fault detection, with the classification accuracy reaching 93.4% and fault detection rate 92.0%, and the false positive rate is kept at a low level. It improves the efficiency and accuracy of power fault detection, and provides a reference for the application of artificial intelligence in power system. The research results are of great significance for optimizing the fault handling process of power system and improving power safety. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Diagnostyka is the property of Polish Society of Technical Diagnostics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=190225093
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        Value: 10.29354/diag/214218
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 12
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Electricity safety
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      – SubjectFull: Classification
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      – SubjectFull: Electronic data processing
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      – SubjectFull: Feature extraction
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      – SubjectFull: Electric power systems
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      – SubjectFull: Artificial neural networks
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              Text: 2025
              Type: published
              Y: 2025
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