氣體絕緣開關設備因電、熱或人為在製造過程中的疏失，導致在電力系統運轉的過程中發生故障。其中造成氣體絕緣開關絕緣劣化的主要原因有：絕緣材質表面污垢造成之沿面放電、存在於絕緣材質中的雜質及空洞造成內部放電、現場組裝或施工不良，造成尖端而成形之電暈放電，以及因內部放電劇烈形成電樹通道放電等。由於不同瑕疵類型會產生不同的局部放電特徵，故可對運轉中的電力設備利用量測儀器檢測局部放電後進行分析，以達到預防性設備故障診斷的目的，其將可避免引起不必要的停電及損失。因此，評估氣體絕緣開關設備中可能存在的瑕疵類型，即成為本研究之重要課題。本文初步選用三台氣體絕緣開關分別在封裝前於內部預製不同瑕疵類型，藉由施加不同試驗電壓測得各類瑕疵的局部放電記錄資料後，利用數種統計方法萃取出特徵參量做為適應性類神經模糊推論系統(adaptive neuro-fuzzy inference system, ANFIS)的輸入資料，以評估辨識的效果。由ANFIS為核心組成的瑕疵辨識系統分析結果顯示，各類瑕疵類型的平均辨識率皆達90%以上。本文提出的系統架構除可持續累積氣體絕緣開關設備的瑕疵量測資料庫外，同時可做為後續其它電力設備瑕疵辨識系統建構的參考依據。

Partial discharge (PD) is the main cause of degradation of the insulation in gas-insulated switchgear (GIS). PD phenomena include: surface discharge, cavity discharge, corona discharge, and treeing channel discharge. Previous research has shown that different types of defects in GIS generate different symptoms of PD, which are associated with various degrees of damage to the GIS. Hence, PD detection is essential to the reliable evaluation of insulation systems and the identification of defects in GIS. In this research, the experimental objects were GIS defect models, which were filled with SF6 gas. Three models were designed based on the results of investigations of numerous power equipment failures. Statistical features were extracted from the PD pattern data and were inputs of adaptive neuro-fuzzy inference system (ANFIS). The results reveal that ANFIS classification has a high success rate, reaching an acceptable classification accuracy 90%. In addition to accumulating a huge mass of PD data of GIS, the procedure that proposed in this study can be also used to develop a data base of defect recognition for other power equipment.

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