| 摘要: |
| 为快速、准确预测接触网复合绝缘子临界污闪电压,减少人工污秽试验工作量,提出了一种复合绝缘子污秽闪络电压预测模型。首先,利用黄金正弦（golden sine algorithm,GSA）与分段线性混沌映射（piecewise linear chaotic map,PWLCM）改进的减法平均优化器（subtraction average based optimizer,SABO）算法增强反向传播（back propagation,BP）神经网络的性能;其次,利用人工污秽试验获取10种不同复合绝缘子的闪络电压,收集相关试验参数;再次,依据Obenaus模型对复合绝缘子污秽闪络表现进行分析,利用斯皮尔曼（Spearman） 相关系数法筛选出与复合绝缘子临界污闪电压密切相关的4个参数作为预测模型的输入特征量,以预测复合绝缘子临界污闪电压;最后,利用五折交叉验证法对预测模型进行综合评估,并与常用智能优化算法预测模型的预测结果进行比较。结果表明: GSABO-BP模型预测复合绝缘子污闪电压平均绝对误差为1.244 kV,平均绝对百分比误差为2.25%,模型可决系数稳定在0.98以上；与改进前的SABO-BP模型相比,预测值平均误差下降67.80%。GSABO-BP模型在复合绝缘子污闪电压预测上具有较高的预测精准度,对电气化铁路供电系统的防污保护工作具有重要意义。 |
| 关键词: 接触网 复合绝缘子 相关系数法 污闪试验 闪络电压预测 |
| DOI：10.11918/202406001 |
| 分类号:TM216 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(52067013);中国铁路总公司科技研究开发计划资助项目(2017010-C) |
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| Improved SABO-BP model for predicting pollution flashover voltage of catenary composite insulators |
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WANG Sihua,MA Shengyi
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（School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China）
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| Abstract: |
| To quickly and accurately predict the critical flashover voltage of composite insulators in catenary systems and reduce the workload of artificial pollution tests, a prediction model for composite insulator pollution flashover voltage is proposed. First, the performance of the back propagation (BP) neural network is enhanced using the subtraction average based optimizer (SABO) algorithm improved by the golden sine algorithm (GSA) and piecewise linear chaotic map (PWLCM). Second, artificial pollution tests are conducted to obtain the flashover voltage of 10 different composite insulators, and relevant test parameters are collected. Third, the Obenaus model is used to analyze the pollution flashover behavior of composite insulators, and the Spearman correlation coefficient method is employed to select 4 parameters closely related to the critical flashover voltage of composite insulators as input features for the prediction model. Finally, the prediction model is comprehensively evaluated using five-fold cross-validation and compared with prediction results from commonly used intelligent optimization algorithms. The results show that the GSABO-BP model predicts the flashover voltage of composite insulators with an average absolute error of 1.244 kV, an average absolute percentage error of 2.25%, and a coefficient of determination consistently above 0.98. Compared to the original SABO-BP model, the average prediction error is reduced by 67.80%. The GSABO-BP model demonstrates high prediction accuracy for the flashover voltage of composite insulators, which is significant for the anti-pollution protection of electrified railway power supply systems. |
| Key words: catenary composite insulator correlation coefficient method pollution flashover test flashover voltage prediction |
