地下電纜為電力系統主要設備，雖不易遭受天然災害之破壞，一旦發生故障，除了維修耗時外成本相對昂貴，並且影響民生用電非常明顯，故檢測地下電纜局部放電的技術就相對重要。本研究為探討地下電纜直線接頭絕緣狀態之研究，使用局部放電檢測能依其絕緣之狀態適時進行維修，以避免非預期停電發生。
本研究採用2種瑕疵共15組地下電纜接頭絕緣破壞試驗資料，藉由解剖電纜試體後觀察破壞路徑，分析與歸類電纜異常種類，並經過雜訊抑制與特徵萃取等步驟取出104種特徵參數，由中挑選6種特徵值組合成5組雙特徵值觀察，發現各組地下電纜接頭的加速劣化試驗，局部放電的趨勢呈現不規則性，難以整理出通用規則適用於所有試驗資料。採用findchangepts函數對15組試驗資料雙特徵曲線尋找絕緣狀態變化之轉折點，取代以往採用放電次數斜率變化的判斷方式，再將所有試驗資料彙整後以隨機方式分為訓練及測試資料，訓練資料運用支持向量機演算出分類超平面後，再以測試資料驗證預測成功率，最終發現「正放電區域放電次數之相位重心」與「正放電區域的放電次數-高度分布之標準差」為本研究表現最佳之特徵值，經過驗證可在絕緣破壞前預測進入危險區，成功率可達82%，分類超平面後續亦可由曲線擬合工具導出多維方程式，未來可使用在監測儀器達成現場判斷電纜狀態的效果。

Underground cable is the main equipment of power system .It is not easily damaged by natural disasters. Once the fault occurs, in addition to the maintenance time, the cost is relatively expensive, and the impact on people's livelihood electricity consumption is very obvious. Therefore, the method of detecting partial discharge of underground cable is relatively important.This research is to study the insulation state of the underground cable straight joint. Partial discharge detection can be used to evaluate the insulation state of underground cables, so as to avoid unexpected accidents.
In this paper, two kinds of 15 sets of underground cable joint insulation damage test data were used. After dissecting the cable test body, the damage path was observed, the abnormal types of cable were analyzed and classified, and 104 characteristic parameters were taken after noise suppression and feature extraction. Finally, the phase center of gravity and standard deviation of the number of discharges in the positive discharge area are selected as the characteristic values of the main use. It is found that the accelerated deterioration test of each group of cable joints shows that the trend of partial discharge is irregular, and it is difficult to sort out the general rules for all test data. In this study, the findchangepts function is used to define the turning point of the change of the insulation state. Instead of using the judgment method of the slope change of the discharge times, the double characteristic curves of the 15 sets of cable test data are initially defined in the attention area and the danger zone, and then all the data are collected. The random method is divided into training and test data. After the training data is calculated by the support vector machine to calculate the classification hyperplane, the test data is used to verify the prediction success rate. After verification, the risk can be predicted before the insulation failure, and the success rate can reach 82%. The classification hyperplane can also be followed by a curve fitting tool to derive multi-dimensional equations, which can be used in the future to determine the effect of the cable state on the site.

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