.png){kind=icon}
.png){kind=icon}
Return
Missing Data Imputation Algorithm for Transmission Systems Based on Multivariate Imputation With Principal Component Analysis
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3194545.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As the importance of utility condition is increasingly acknowledged, the use of asset management technologies in the electric power industry has rapidly grown. The global trend of asset management follows risk management that accounts for the probability and consequences of failures. Because asset management systems tend to be composed of various legacy systems, each of which is installed and designed to collect data according to a certain data type and acquisition purpose, it is necessary to develop a system that cleans and integrates data acquired from each legacy system. This study explores the development of an asset management system for a transmission system as a representative linear asset consisting of different segments in a sequence. First, the configurations and characteristics of linear asset datasets are analyzed. Second, an automatic data cleaning system, equipped with six types of data cleaning functions for extracting dirty data from entire datasets, is proposed. An algorithm for data imputation, which is essential for estimating the remaining useful life, is developed based on principal component analysis-iterative algorithm (PCA-IA). Afterward, the performance of the proposed system is verified using actual data with the help of the Korea Electric Power Corporation (KEPCO). Specifically, to evaluate the performance of the proposed system, an automatic cleaning process is demonstrated using actual legacy datasets.
Keywords:
Aging
Cleaning
Power systems
Oil filled cables
Communication cables
Maintenance engineering
Asset management
Transmission system
data cleaning
database management
data imputation
principal component analysis
linear asset
machine learning
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
