Intelligent fault diagnosis is realized at the State Grid substation maintenance center to eliminate the need for manual interference.
The time for searching for fault information is shortened from a few minutes to less than 1 minute. Fault forms are generated with just one click to reduce the workload of information collection by 85% for a single fault.
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Introduction

A substation maintenance center of State Grid
主要负责变电站、开开关站等变配电设备的安装、调试、维护等,一般有高压组,检修断路器、变压器、互感器、等一次设备;二次维护组,维护继电保护、控制回路、测量回路等二次回路;微机组,微机联网、监控、操作、连锁等。
Project Results
  • 1. Accumulation and inheritance of expert experiences

    Expert experiences in technical standards, fault investigation and analysis reports, and other unstructured documents are annotated and extracted based on the NLP technology for the structurization and systemization of achievements, and extraction and online sharing of knowledge. 

    2. Correlation and integration of line fault information

    The heterogeneous data from multiple sources including equipment ledgers, fault history, online monitoring systems, and fault fixing records is connected and integrated to create the knowledge graphs of line faults and fixing procedures, which present full pictures of fault cause analysis, locating, troubleshooting and solutions. 

    3. Intelligent Q&A on line faults

    Interactive intelligent Q&A in the context of various scenarios is supported based on semantic comprehension and intention recognition technologies to provide the applications such as FAQs, line information retrieval, accurate fault search, and fault diagnosis. User input in the verbal Q&A process is analyzed for accurate determination of user needs and quick retrieval of transmission line journals and fault information, with the search time shortened from 10 minutes to 1 minute. 

    4. Quick generation of fault forms

    When a line fault occurs, the related information is automatically collected and organized using the predefined templates for easy analysis and determination of the cause of fault, with a fault report generated automatically. The workload of information collection for each fault is reduced by 85%, and the intelligent fault diagnosis is realized without the need for manual intervention.

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