以支撐向量機為基礎之電力系統故障事件與位置辨識

Detection of Power System Fault Events and Location with Support Vector Machine-based Approach

賴昶嘉(Lai, ChangChia)

指導教授 ： 張文恭

國立中正大學/工學院/電機工程學系/碩士(2012年)

摘要

任何電力品質擾動使得電壓或是電流偏離額定值，可能導致系統上的誤動作或是用戶端的設備嚴重損害，因此輸電線路的故障檢測和分類，是電力系統中很重要的一環。在電力系統上的故障分為三相平衡故障與不平衡故障。不平衡故障分別為:單線對地故障、線對線故障、與雙線對地故障。而線路的故障位置，也是電力系統中很重要的一環，快速的診斷出故障位置，可以節省人力上的調動、減少停電時間以及停電時間上的損失。
本論文的著重於SVM的分類技術，此技術在文中用來分類故障類型以及故障定位，並帶入新的特徵選擇，此特徵選擇將故障的特性放大，因而提高分類的準確度

關鍵字

電力品質 ； 電力系統 ； 支撐向量機 ； 不平衡故障 ； 故障定位

並列摘要

Voltage and current deviations from their nominal values may result in serious damage or equipment malfunction. Therefore, the transmission line fault detection and classification are very important to the power system study. Commonly seen failures in the power system are three-phase balanced and unbalanced faults. There are several types of three-phase unbalanced faults, including single line-to ground, line-to-line, and double line-to-ground faults. Tracking the location of the fault line is also very important in the power system. Rapid diagnosis of the fault location can save manpower, reduce outage times, as well as losses.
This thesis aims at applications of the support vector machine (SVM) classification techniques. The SVM is used to identify the fault event and the fault location. The proposed new feature selection can reduce the SVM training time. Also, this feature selection amplifies the characteristics of each fault event. Thus, it improves the SVM classification accuracy.

並列關鍵字

Power quality ； power systems ； support vector machines ； unbalanced fault ； fault location

參考文獻

[1]L. Hamel, ”Knowledge Discovery with Support Vector Machines,” Willy, New Jersey, Unit State, 2009.

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