Acoustic Fault Detection Technique for High-Power Insulators
Acoustic Fault Detection Technique for High-Power Insulators
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DOI：
10.1109/tie.2017.2716862
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发表时间：
2017-12-01
影响因子：
7.7
通讯作者：
Yoon, Jong Rak
中科院分区：
文献类型：
--
作者：
Park, Kyu-Chil;Motai, Yuichi;Yoon, Jong Rak
绝缘子是高压输电系统中用于电气隔离和机械固定电线的重要设备。由绝缘子劣化引起的故障会给输电线路带来非常严重的问题。介绍了利用声辐射噪声进行绝缘子故障声学检测的技术。在电波暗室中测量了正常状态和故障状态绝缘子的辐射噪声。使用的绝缘子是两个瓷绝缘子，一个断路开关，两个线路海报和一个避雷器。一种新的声学技术确定方向的绝缘子故障使用源定位与三维麦克风阵列。其优点是分类故障状态绝缘子，而无需人工检查，考虑的总噪声和120 Hz谐波分量的量。采用神经网络确定故障检测点，实现状态自动诊断.所提出的技术进行了评估，不同的，真实的数据集和有效性进行了验证。在三种典型工况下，噪声源检测准确率为100.0%，分类正确率达到96.7%。
Insulators are important equipment used to electrically isolate and mechanically hold wires in high-voltage power transmission systems. Faults caused by the deterioration of the insulators induce very serious problems to the power transmission line. Techniques were introduced for the acoustic detection of insulator faults by acoustic radiation noises. Radiation noises were measured from normal state insulators and fault state insulators in an anechoic chamber. The insulators used were two porcelain insulators, a cut-out switch, two line posters, and a lightning arrester. A new acoustic technique determines the direction of the insulator faults using source localization with three-dimensional microphone arrays. The advantage is to classify the fault state insulators without human inspection by considering the amount of total noises and 120-Hz harmonic components. The fault detection was determined by neural network to diagnose the state automatically. The proposed technique was evaluated by distinct, real datasets and the efficacy was validated. The noise source was detected with 100.0% accuracy and the classification ratio achieved 96.7% for three typical conditions.