Asset management technology is rapidly growing in the electric power industry because utilities are paying attention to which of their aged assets should be replaced first. The global trend of asset management follows risk management that comprehensively considers the probability and consequences of failures. In the asset management system, the risk assessment algorithm operates by interfacing digital datasets from various legacy systems. In this study, among the various electric power assets, we consider transmission cable systems as a representative linear asset consisting of different segments. First, the configurations and characteristics of linear asset datasets are analyzed. Second, six types of data cleaning functions are proposed for extracting dirty data from the entire dataset. Third, three types of data integration functions are developed to simulate the risk assessment algorithm. This technique supports the integration of distributed asset data in various legacy systems into one dataset. Finally, an automatic data cleaning and integration system is developed and the algorithm could repeat the cleaning and integration process until data quality is satisfied. To evaluate the performance of the proposed system, an automatic cleaning process is demonstrated using actual legacy datasets.
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.
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