[4M3-GS-13] AI application: Electric power

[4M3-GS-13-01] Outlier detection for hydroelectric power plant operation data.

~Comparison of characteristics of various outlier detection methods.~

〇Risa Watanabe¹, Takahiro Nishigaki¹, Takashi Onoda¹ (1. Aoyama Gakuin University)

Keywords:Outlier Detection

In recent years, electric power companies collects different types of sensor data and weather information to maintain the safety of hydroelectric power plants while the plants are in operation. Although the power plant operation data is mostly normal state data, there is little accumulation of abnormal state data, and it is not easy to observe data related to abnormal states. Therefore, we have to identify malfunction signs from among the collected sensor data. In this paper, we detected outliers from hydropower plant operation data using five outlier detection methods including one-class SVM and compared the characteristics of each outlier.