本論文應用高頻比流器、耦合電容感測器及音射感測器來檢驗GIS局部放電的情形，並可辨識顆粒撞擊、震動或局部放電的信號。使用三組音射感應器與高頻比流器做信號量測，已收到信息的時間差，作為GIS局部放電位置判斷的依據。當有粒子撞擊發生時，從三組音射感測器接收到信號的時間差，計算出音源距三組音射感測器的在氣體絕緣開關外殼上的位置。如果局部放電發生，由三組音射感應器接收到的信號，定位出局部放電最先傳到氣體絕緣開關之管壁的位置，而音射感應器接收到的信號和高頻比流器(或耦合電容)接收的電氣信號時間差，為局部放電時音波的傳遞時間，由放電的位置到最近的管壁再到音射感測器的時間，由此可計算出局部放電位置和管壁的距離。
測量相同的局部放電信號源，使用不同的感應器或量測系統測量信號時，其頻譜特性分析卻不相同，導致在分析局部放電的信號時很難比較。因此本論文提出重建局部放電源信號的方法，使用快速傅立葉轉換技術，將不同感測器或測量系統測量的局部放電源做重建。結果顯示確實可有效應用於線上測量及各種局部放電的信號分析。
在本研究裡，我們設計三種不同的瑕疵，分別在三台氣體絕緣負載斷路開關內，以高頻比流器測量放電的信號。放電信號經由離散小波轉換過濾雜訊，其信號的峰態和偏態的變化，用機率神經網路和模糊C均值演算法分類，雜訊比70 dB以上比較低雜訊時，其機率神經網路和模糊C均值演算法分類可以接近100%準確的分類出不同的瑕疵氣體絕緣負載開關。實驗結果驗證概率神經網路和模糊C均值演算法應用到不同瑕疵氣體絕緣負載開關局部放電的識別是很有效的方法。

In this thesis, the electrical and non-electrical methods are both applied in the examinations. The electrical method applies high frequency current transformer (HFCT) and capacitive coupler(CC)measurements, and the non-electrical method applies the acoustic emission (AE) measurement. According to the measured data, they are applied to identify the particle impact, vibration, or partial discharge (PD) inside gas insulated switchgear (GIS). Three AE sensors are stuck as a triangle on GIS crust and measure the sound signals. As particle impact occurs, the location of GIS crust is positioned according to the time difference of three AE signals. If PD occurs, we identify the PD according to the time difference of the AE signals and electric signals by HFCT or CC sensors. With the time difference and the distance detected by the three AE sensors, one can locate the position of PD source on the GIS crust.
For a PD source, the measured signals vary greatly among different measuring sensors and/or systems. The characteristics of the spectrums of the signals are different with different measuring sensors and/or systems. This makes it difficult to analyze and compare PD signals measured by using different systems. The study can satisfactorily restore the PD sources signals by operating the transferring function on those PD signals measured from different HFCT measuring systems. Applying this technique to on-line measurements, the PD source signals can also be reconstructed effectively. This thesis proposes an approach to determine the classification of PD events in Gas Insulated Load Break Switches (GILBS).
Three kinds of different defects are designed and placed inside three GILBS individually. Discrete wavelet transform (DWT) is employed to suppress noises of measured signals by the HFCT. The skewness and kurtosis of the PD signals are adopted as input variables of Probabilistic neural networks (PNN) and fuzzy C-means (FCM). PNN and FCM clustering approach are used to classify the methods. The noise range is over 70 dB, which belongs to the low noise measurement environment. Classification correctness ratios of the defect models are obtained using PNN and FCM, they are all close to 100 %. For accurately determination of the different defect model, feature extraction and statistics analysis of the measured signals are used in the proposed method. Finally, experimental results validate that the proposed approach can effectively discriminate the PD events in GILBS.

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