A study on the method of automatic generation of distribution equipment health index for asset management

Failure of power facilities causes huge losses and inconvenience to customers. Therefore, Power system companies investing to the facilities for stable power supply. It called 'Power System Asset Management' and this means predicting the status of power facilities using data from assets (operation, maintenance, failure, etc.) and establishing an optimal investment plan to improve management efficiency. In the case of power distribution facilities in Korea, old facilities and high risk facilities are replaced through diagnosis of power distribution facilities and evaluation of asset health to minimize customer damage to and reduce investment costs. For Asset status prediction, it need variable input data which affect power facilities and analysis of the impact on power facilities is also essential. However, it is not sufficient for research that comprehensively analyzes the assessment factors of distribution facilities' asset health index and the effects of the facilities to form an health index table. Therefore, in this paper, we study methods for efficient implementation of asset health assessment schemes. Based on big data analysis and machine learning algorithms, we developed a function to automatically extract and assign scores to evaluation items that are highly related to the lifespan of the facility. © 2021 Korean Institute of Electrical Engineers. All rights reserved.