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Suzhou Electric Appliance Research Institute
期刊號: CN32-1800/TM| ISSN1007-3175

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基于信息融合的變壓器故障多級診斷方法

來源:電工電氣發布時間:2019-06-13 14:13 瀏覽次數:55
基于信息融合的變壓器故障多級診斷方法
 
張愛蘭,許志元,楊琦欣,劉春明,朱彥瑋,李曉磊
(國網山東省電力公司濟南供電公司,山東 濟南 250012)
 
    摘 要:建立了基于信息融合的變壓器故障多級診斷模型,該模型融合了在線監測、油中溶解氣體、電氣試驗等多源數據信息。采用自適應遺傳算法優化的小波神經網絡對變壓器故障進行初級診斷,通過改進D-S證據理論對初級診斷結果進行決策級融合,實現對變壓器故障的深度診斷與定位。通過應用實例證明,該方法可以有效提高變壓器故障診斷的精度和可信度,減小診斷的不確定性。
    關鍵詞:變壓器故障;多級診斷;改進D-S證據理論;信息融合
    中圖分類號:TM411     文獻標識碼:A     文章編號:1007-3175(2019)06-0015-06
 
Multi-Level Diagnosis Method of Transformer Fault Based on Information Fusion
 
ZHANG Ai-lan, XU Zhi-yuan, YANG Qi-xin, LIU Chun-ming, ZHU Yan-wei, LI Xiao-lei
(State Grid Shandong Electric Power Company Jinan Power Supply Company, Jinan 250012, China)
 
    Abstract: This paper established a multi-level diagnosis model of transformer faults based on information fusion. This model integrated multi-source data information in transformer faults, such as the on-line monitoring data, dissolved gas in oil and electrical test. The adaptive genetic algorithm was adopted to optimize the wavelet neural network, so as to implement primary diagnosis of transformer faults. The improved D-S evidence theory was used to carry out decision-Level fusion of primary diagnostic results to realize the depth diagnosis and location of transformer faults. The application example shows that this method can improve the accuracy and reliability of transformer fault diagnosis, reducing the diagnostic uncertainty.
    Key words: transformer fault; multi-level diagnosis; improved D-S evidence theory; information fusion
 
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