全火焰桥接条件下棒板间隙的直流击穿电压预测

Fire Pub Date : 2024-04-16 DOI:10.3390/fire7040143
Ziheng Pu, Yuan Li, Peng Li, Kuan Ye, Kai Zhou, Ruizhe Zhang
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引用次数: 0

摘要

为了评估野火导致输电线路跳闸的风险,有必要预测火焰下绝缘间隙的击穿电压。本文首先研究了木箱全火焰桥接下杆面间隙的击穿预测,然后考虑了不同尺寸的木箱和不同间隙距离,得到了全火焰桥接的击穿电压和泄漏电流值。然后,进行多物理场模拟,获得火焰间隙特征参数,如空间温度。对特征量进行归一化和降维处理,并建立了基于支持向量机(SVM)的火焰条件下间隙击穿电压预测模型。最后,将不同火焰间隙条件下的直流耐压值和相应的特征量作为样本集来测试预测模型。结果表明,小间隙击穿电压的预测误差小于 2.6%。样本在不同火焰强度下进行了训练和预测测试,误差小于 3.3%。用 30~60 cm 的小间隙数据预测 100~140 cm 的长间隙击穿电压,误差小于 3.2%。与现有研究中提出的拟合修正公式法相比,误差分别减少了 11.5%和 4.4%,验证了 SVM 预测模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of DC Breakdown Voltage of Rod–Plate Gaps under Full-Flame Bridging Conditions
In order to evaluate the risk of transmission line tripping due to wildfires, it is necessary to predict the breakdown voltage of the insulation gap under the flame. Firstly, this paper studies the breakdown prediction of rod–plane gaps under the full-flame bridging of wooden cribs; it then obtains the breakdown voltage and the leakage current values of full-flame bridging considering different sizes of wooden cribs and different gap distances. Then, a multi-physical field simulation is carried out to obtain the flame gap characteristic parameters, such as spatial temperature. The feature quantity is normalized and reduced in dimension, and a prediction model for gap breakdown voltage under flame conditions based on a support vector machine (SVM) is established. Finally, the DC withstand voltage values and corresponding characteristic quantities under different flame gap conditions are used as sample sets to test the prediction model. The results show that the prediction error for small gap breakdown voltage is less than 2.6%. The samples were tested under different flame intensities for training and prediction, and the error was less than 3.3%. The small gap data for 30~60 cm is used to predict the breakdown voltage of the long gap for 100~140 cm, and the error is less than 3.2%. Compared with the fitting correction formula method proposed in existing research, the error is reduced by 11.5% and 4.4%, respectively, which verifies the effectiveness of the SVM prediction model.
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