電力設備的絕緣狀態直接影響系統供電的穩定性，而絕緣材料之老化為電力設備於運轉過程中的一種必然現象，因此有效地絕緣狀態之診斷技術對於系統運轉的可靠度提升，扮演著舉足輕重的角色。基於此，本文旨在運用脈衝電流法，探討及診斷模鑄式比流器之絕緣老化狀態。首先，利用加壓實驗進行局部放電檢測，對於檢測試驗之物理量再進行量化。其次，應用統計運算之指紋圖譜，做為老化試驗特徵的萃取方式，在擷取特徵中發現最大放電量、平均放電量與脈衝次數之偏態、峰態、交叉關聯與不對稱度，並加上直接量測被試物的起始電壓、熄滅電壓、總放電量及放電次數，以上均出現明顯的老化趨勢。最後，運用本文所觀測得到之特徵參數，做為類神經網路辨識系統的輸入資料，進而辨識絕緣老化之分類。研究結果顯示，由上述特徵之趨勢現象做為分類與辨識的參考依據，準確性高達98.87%辨識效果，因此本研究所獲致的結果，將可提供日後做更深入研究之相關資訊。

The insulation status of electrical equipments directly influences the stability of power system. However, the aging phenomenon of insulation materials during running period is a natural property. Thus effectively insulating diagnostic technology plays an important role to enhance the system operating reliability. Therefore, this thesis proposed a diagnosis method to classify the insulation aging state of cast-resin current transformer by current pulse method of partial discharge. First, the high voltage experiment with aging acceleration process is carrying out to get the physical values about partial discharge of defected current transformer. Secondly, applying statistical methods to the collected data, specific fingerprints can be extracted to represent the aging status. It is found that including the skewness, kurtosis, cross-correlation and asymmetry of maximum discharge value, average discharge value and discharge counts, the inception and extinction discharge voltage, the total discharge value and frequency, they have similar and obvious aging trend. Finally, a neural network identification system to identify the insulation aging state is proposed. The extracted features and parameters are treated as the input data and the flexible back propagation training method is used to accelerate the convergence process. The simulation results show that the proposed features can effectively represent the aging state and over 98% recognition rate can be got. The promising result of this thesis can provide the important information for further research in future.

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