基于多模型融合的电网故障抢修时长预测

doi:10.11959/j.issn.1000-0801.2020016

Abstract

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.

Key words: grid fault, repair time, LSTM, LightGBM, XGBoost

CLC Number:

TN929.5

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.

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统计指标	未过滤	过滤时长超过1天数据	过滤时长超过6 h数据
数据总数	201 412	200 916	194 333
过滤数据量 —		496(0.25%)	7 079(3.5%)
抢修时长均值	69.65	63.07	43.57
抢修时长标准差290.84		131.09	58.47
抢修时长最大值44 718		1 439	359

算法类型	平均绝对误差MAE
LightGBM	1.21
XGBoost	1.52
LSTM	1.23
加权结果	1.15

Prediction of power grid fault repair time based on multi-model fusion

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