Prediction for Substation Equipment Failure Rate Based on Improved Grey Combination Model

被引：0

作者：

Wu G. ^{[1
]}

Ni X. ^{[1
]}

Song Z. ^{[1
]}

Gao B. ^{[1
]}

机构：

[1] School of Electrical Engineering College, Southwest Jiaotong University, Chengdu

来源：

Ni, Xuesong (twocargo@126.com) | 1600年 / Science Press卷 / 43期

关键词：

Accumulative failure; Characteristics point detection; Failure data partition; Failure demarcation point; Failure rate prediction; Grey linear regression combination model; Initial data preprocessing;

D O I：

10.13336/j.1003-6520.hve.20170628021

中图分类号：

学科分类号：

摘要：

When the existing failure prediction models of substation equipment are used to predict failure rate, such results often appears that the fault stabilization period data are higher than the actual value and the fault loss period data are lower. For this phenomenon, on the basis of the research of development process of substation equipment failure, we introduced two concepts of "fault demarcation point" and "failure data partition". Moreover, by combination with gray-linear regression model, we established a new optimization model of substation equipment failure rate prediction. Through numerical validation to the discussion of various characteristics and effectiveness of various models, the results show that fault demarcation point and failure data partition are both conducive to improving the accuracy of substation equipment failure prediction in the case which have two stages of the period of stability and loss of the fault rate. The relative error rate of improved model is 3.59% lower than that of the gray linear regression model and also 3.91% lower than that of the fault forecast model based on M-R algorithm, and the fitting effect of optimization model is better than others. © 2017, High Voltage Engineering Editorial Department of CEPRI. All right reserved.

引用

页码：2249 / 2255

页数：6