摘要
准确获取空气间隙的放电电压是高电压工程领域长期追求的目标,开展放电电压预测研究具有重要的工程意义。基于数据驱动的思想,以750 kV同塔双回输电线路和1 000 k V交流紧凑型输电线路杆塔空气间隙为训练样本,通过电场计算获取了间隙的空间电场分布;然后从高压端金具到塔身的最短路径上提取电场特征量作为输入量用以表征间隙结构,并采用支持向量机建立了杆塔空气间隙标准操作冲击放电电压预测模型;最后对1 000kV酒杯塔边相空气间隙的放电电压进行了预测。与放电特性试验结果对比表明:4～8 m长间隙放电电压预测值的最大误差在10%以内,平均绝对误差为3.66%,并验证了预测方法的有效性。该方法以多种杆塔结构空气间隙放电试验数据为基础,利用机器学习手段预测得到了另外一种杆塔间隙的放电电压,为获取输变电工程间隙的放电电压提供了一种新的思路。
It is a long term goal for high voltage engineering field to acquire the discharge voltage of air gap accurately,and the research for discharge voltage prediction has important engineering significance. Based on data driven theory, the line-tower air gaps of 750 kV one tower double-circuit and 1 000 kV compact tower transmission lines were selected as training samples, and the electric field distribution of the gap was calculated. In order to characterize the structure of air gaps electric field features were extracted along the shortest path from the high voltage end fitting to the tower as input parameters. A discharge voltage prediction model of line-tower air gaps under standard switching impulse was built using support vector machine. Finally, the discharge voltages of UHV cup-tower outer phase air gaps were predicted. Compared with the experimental values in discharge characteristic test, the maximum error of predicted values with gap distances between 4 and 8 m is within 10%, while mean absolute error is 3.66%. The results verify the effectiveness of the prediction method. Based on discharge test data of various tower structures, the discharge voltage of other line-tower air gaps can be predicted by machine learning algorithm, which can provide a new way for the acquisition of discharge voltages in engineering gap.
引文
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