首页> 外文会议>Asia Conference on Power and Electrical Engineering >Risk Modeling of Transmission Line Defects and Forecasting Method Based on SVM
【24h】

Risk Modeling of Transmission Line Defects and Forecasting Method Based on SVM

机译:基于支持向量机的输电线路缺陷风险建模与预测方法

获取原文

摘要

Establishing indicators for evaluating and forecasting the overall defect status of transmission lines are of great significance for the operation and maintenance, while studying the impact of different external factors on the development process of transmission line defects is also important. Therefore, this paper proposes a transmission line defect risk model and a SVM based prediction method. By dividing the transmission line into several component according to its own structure, the definition of the defect risk index is given and used to evaluate the status of the transmission line defects, which is based on the defect severity quantification and the membership degree analysis of each component. The historical defect risk value samples are calculated based on the historical defect data, and then use the Pearson correlation coefficient to select the meteorological factors with large impact on the defect development process to each component of the transmission line. Construct training samples with related factors, build a RBFSVM based transmission line defect risk value prediction model, and predict the defect risk value of the transmission line in the future. An example analysis is conducted on the transmission lines of Guangdong power grid to predict the defect risk value of a transmission line in each month of 2018. The results show the feasibility and accuracy of the proposed model and forecasting method in this paper.
机译:建立评价和预测输电线路整体缺陷状态的指标,对运行和维护具有重要意义,同时研究各种外部因素对输电线路缺陷发展过程的影响也很重要。因此,本文提出了一种传输线缺陷风险模型和一种基于支持向量机的预测方法。通过根据传输线自身的结构将传输线分为几个部分,给出缺陷风险指数的定义,并根据缺陷严重性的量化和对每个成员的隶属度分析,对传输线缺陷的状态进行评估。成分。根据历史缺陷数据计算历史缺陷风险值样本,然后使用Pearson相关系数选择对传输线各组成部分的缺陷发展过程有重大影响的气象因素。构建具有相关因素的训练样本,建立基于RBFSVM的输电线路缺陷风险值预测模型,并预测未来输电线路的缺陷风险值。通过对广东电网的输电线路进行实例分析,预测了2018年每个月的输电线路缺陷风险值。结果证明了本文提出的模型和预测方法的可行性和准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号