基于改进BP神经网络的变电站检修运维成本预测
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熊一(1988-),男,博士,主要从事电网管理等研究;E-mail:675786099@qq.com

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TM-9

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国网湖北省电力有限公司科技项目(52153820000A);国家自然科学基金(91746118);湖南省自然科学基金(2019JJ40302)


Overhaul operation and maintenance cost prediction of substation based on improved BP neural network
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    摘要:

    变电站的检修运维成本受众多复杂因素影响,且检修费用数据记录具有模糊性和波动性。为解决检修费用记录不明的问题,首先对变电站检修条目划分并采用水平和垂直方向的数据分析方法进行处理,再利用 BP神经网络预测检修运维成本。为提高 BP神经网络预测精度,采用 K-fold交叉验证对原始数据训练模型进行精准调整,应用遗传算法对 BP神经网路的初始值和阀值进行调整和改进,从而建立基于遗传算法的改进 BP神经网络检修运维成本预测方法。以某地市变电站为例进行变电检修运维成本预测,对比分析显示所提方法能有效提高模型预测精准度,从而为电网给变电站拨付检修费用提供参考价值。

    Abstract:

    Overhaul operation and maintenance costs of substations are affected by many complicated factors. Fuzzy and fluctuating data records of maintenance costs worsen the situation. In order to solve the unclearness of the overhaul cost record, this paper firstly divides the substation maintenance items and uses data analysis method of horizontal and vertical direction to process the items. Then BP neural network is used to predict the maintenance cost. In order to improve the accuracy of BP neural network prediction,Kfold crossvalidation is used to accurately adjust the original data training model. The genetic algorithm is used to adjust and improve the initial value and threshold of the BP neural network. Therefore an improved BP neural network maintenance and operation cost prediction method is established based on the genetic algorithm. Taking a substation in a certain city as an example to predict the operation and maintenance cost of substation maintenance, comparative analysis shows that the proposed method can effectively improve the accuracy of model prediction, thereby providing reference value for the power grid to allocate maintenance costs to substations.

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熊一,詹智红,柯方超,周秋鹏,孙利平,廖爽,任羽纶,周任军.基于改进BP神经网络的变电站检修运维成本预测[J].电力科学与技术学报,2021,(4):44-52. Xiong Yi, Zhan Zhihong, Ke Fangchao, Zhou Qiupeng, Sun Liping, Liao Shuang, Ren Yulun, Zhou Renjun. Overhaul operation and maintenance cost prediction of substation based on improved BP neural network[J]. JOURNAL OF EIECTRIC POWER SCIENCE AND TECHNOLOGY,2021,(4):44-52.

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  • 在线发布日期: 2021-08-28
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