基于多模型融合的电网故障抢修时长预测

潘坚跃; 吴懿臻; 徐汉麟

doi:10.11959/j.issn.1000-0801.2020016

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基于多模型融合的电网故障抢修时长预测

电力信息化专栏 | 更新时间：2024-06-05

- 基于多模型融合的电网故障抢修时长预测
- Prediction of power grid fault repair time based on multi-model fusion
- 电信科学 2020年36卷第1期页码：144-150
- 作者机构：
  
  1. 国网浙江省电力有限公司杭州供电公司，浙江杭州 310009
  2. 浙江华云信息科技有限公司，浙江杭州 310008
- 作者简介：
  
  [ "潘坚跃（1961- ），男，国网浙江省电力有限公司杭州供电公司高级工程师，长期从事数据集成、大数据应用分析方面的工作" ]
  [ "吴懿臻（1984- ），女，浙江华云信息科技有限公司助理工程师，长期从事数据应用分析、数据挖掘方面的工作" ]
  [ "徐汉麟（1990- ），男，国网浙江省电力有限公司杭州供电公司助理工程师，主要研究方向为信息系统及网络安全" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020016
  中图分类号： TN929.5
- 网络出版日期：2020-01，
  
  纸质出版日期：2020-01-20
- 稿件说明：
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潘坚跃, 吴懿臻, 徐汉麟. 基于多模型融合的电网故障抢修时长预测[J]. 电信科学, 2020,36(1):144-150.

Jianyue PAN, Yizhen WU, Hanlin XU. Prediction of power grid fault repair time based on multi-model fusion[J]. Telecommunications science, 2020, 36(1): 144-150.
潘坚跃, 吴懿臻, 徐汉麟. 基于多模型融合的电网故障抢修时长预测[J]. 电信科学, 2020,36(1):144-150. DOI： 10.11959/j.issn.1000-0801.2020016.

Jianyue PAN, Yizhen WU, Hanlin XU. Prediction of power grid fault repair time based on multi-model fusion[J]. Telecommunications science, 2020, 36(1): 144-150. DOI： 10.11959/j.issn.1000-0801.2020016.

摘要

电网故障种类繁多、原因复杂，故障抢修时长的预测相对较难。由于深度学习等新技术的兴起，从故障工单中挖掘有效的信息，进而准确地预测故障抢修时长的方法正在变得可行。以历史电网故障抢修工单为研究对象，提出多模型融合的预测方法，将LightGBM、XGBoost和长短期记忆网络的预测结果进行加权融合。实验结果表明，该多模型融合的预测方法可以较为准确地对故障抢修时长进行预估，为电网故障抢修的自动化和智能化提供更好的支撑。

Abstract

There are many types of power grid faults

and the reasons are complicated.The prediction of fault repair time is difficult.Due to the rise of new technologies such as deep learning

it is feasible to accurately mine the faulty worksheet and accurately predict the fault repair time.Taking the historical grid fault repair worksheet as the research object

the multi-model fusion prediction method was proposed

and the prediction results of LightGBM

XGBoost and LSTM were weighted and fused.The experimental results show that the multi-model fusion prediction method can accurately estimate the fault repair time and provide better support for the automation and intelligence of grid fault repair.

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Keywords

references

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