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研究生:陳基峰
研究生(外文):Chen, Chi-Feng
論文名稱:類神經網路應用於變電所線上故障種類診斷之研究
論文名稱(外文):On-line substation fault type diagnosis by using artificial neural networks
指導教授:諸葛介臣楊宏澤楊宏澤引用關係黃慶連黃慶連引用關係
指導教授(外文):Chieh-Chen ChukoHong-Zen YangMr. Shih
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1993
畢業學年度:81
語文別:中文
論文頁數:77
中文關鍵詞:類神經網路變電所故障種類
外文關鍵詞:Artificial Neural NetworkSubstationFault Type
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變電所故障診斷係配電系統自動化工作極重要的一環。本論文提出一套類
神經網路式專家系統及一套輔助用階層式類神經網路診斷系統,兩者互相
配合於線上對變電所故障進行診斷。本文所提之類神經網路式專家系統架
構與傳統通用型專家系統類似,但在建立與維護上較為簡易。訓練類神經
網路所用之訓練樣本取自故障種類及其相關之一次保護設備,及誤不動作
時之二次後衛保護設備。本診斷系統可用於單一故障、多重故障、保護設
備誤不動作及信號傳輸錯誤等情形下之故障種類判定。並可對其答案作出
推理解釋及提供可信度,以作為操作人員的參考。輔助用階層式類神經網
路診斷系統則直接採用類神經網路的架構,在類神經網路式專家系統無法
提供確切答案或答案可信度相當低時,利用故障電壓及電流波形峰值協助
診斷故障種類。為改善傳統後傳遞理論實用上對大型類比式類神經網路無
法收歛或收歛速度過慢的缺點,本論文採用 Cascade-Correlation的學習
架構。本系統經測試於台電典型二次配電系統,証明其正確性與實用性均
能符合實際電力系統之需求。
On-line fault diagnosis of substation operation is inevitable
in the automation of power system operation. This thesis pro-
poses a new connectionist expert diagnostic system with an
auxiliary artificial neural networks (ANN) diagnostic system
for on-line fault diagnosis of power substation. The
connectionist expert diagnostic system has similar profile of
an expert system, but can be constructed much more easily
from elemental samples. These samples associate fault type with
its primary and secondary protective relays and breakers. This
system can be applicable to the power system control center for
single- or multiple-fault type estimation, even in the cases of
failure operation of relay and breaker, or error-existent data
transmission. Besides , the confidence level of the diagnostic
conclusion and explanation of the answer can be provided to the
users. The auxiliary ANN diagnostic system employs a
hierarchical neu- ral networks structure to help estimate
the fault type when definite conclusion or conclusion with
high confidence level can not be provided by the connectionist
expert system.The peak values of the fault voltage and
current waveforms are used as the input information of the
auxiliary ANN diagnostic system. To improve the efficiency of
the traditional back-propagation training process for large-
size analog ANN,a cascade-correlat- ion learning algorithm
is applied to support the structural implementation of the
system. The proposed approach has been practically verified by
testing on a typical Taipower secondary substation. The test
results, although preliminary, suggest our system can be
implemented by various electric utilities with relatively
low customization effort.
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