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Keywords

substation overhaul operation and maintenance cost prediction, BP neural network, genetic algorithm, Kfold cross validation

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.

DOI

10.19781/j.issn.1673-9140.2021.04.006

First Page

44

Last Page

52

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