基于故障量相对偏移和改进GWO-SVR的输电线路双端故障测距方法
A two-terminal fault location method for transmission lines based on relative deviation of fault quantity and improved GWO-SVR
李铁 1田鹏飞 1孙振庭 2姜志筠 3吴广大 3包鹏宇 3胡钋3
作者信息
- 1. 国网辽宁省电力有限公司,辽宁沈阳 110006
- 2. 国网大连供电公司,辽宁大连 160033
- 3. 武汉大学电气与自动化学院,湖北武汉 430072
- 折叠
摘要
针对基于传统深度学习进行输电线路故障测距时直接采用电压、电流学习而导致模型鲁棒性差的问题,提出了一种采用故障量相对偏移作为故障特征进行学习的方法,该方法显著提高了模型的鲁棒性;针对在通过支持向量回归(support vector regression,SVR)进行深度学习时,传统灰狼优化(grey wolf optimization,GWO)算法对初始参数的寻优容易陷入局部最优问题,提出了一种改进的自适应灰狼优化算法以确定SVR的初始参数.对IEEE33节点模型的仿真结果表明,相较于传统方法,该方法的准确性更高,且当线路电压与长度发生改变时,精度相差甚微,具有较好的鲁棒性.
Abstract
Aiming at the problem of poor model robustness caused by the direct use of voltage and current learning in fault location of transmission lines based on traditional deep learning,this paper proposes a learning method that uses the relative deviation of the fault quantity as the fault feature to learn,which significantly improves the robustness of the model.In view of the fact that the traditional grey wolf optimization(GWO)algorithm is easy to fall into the local optimal problem in the optimization of the initial parameters when using support vector regression(SVR)for deep learning,an improved adaptive gray wolf algorithm is proposed to determine the initial parameters of the SVR.The simulation results of the IEEE33 node model show that compared with the traditional method,the accuracy of the method proposed in this paper is greatly improved,and when the line voltage and length change,the accuracy difference is very small,so the model proposed in this paper has better robustness.
关键词
故障量相对偏移/改进灰狼优化算法/支持向量回归/故障测距Key words
relative deviation of fault quantity/improved gray wolf optimization algorithm/support vector regression/fault location引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据