Data Indicating Malfunction Sign of Hydroelectric Power Plants

Abstract

Kyushu Electric Power Co., Inc. collects different types of sensor data and weather information to maintain the safety of hydroelectric power plants while the plants are in operation. We have to identify malfunction signs from among the collected sensor data. In this paper, we describe a method for identifying the conditions that could cause a malfunction; our method consists of two identification stages. In the first stage, we identify malfunction signs, which are different from normal-condition data, and in the second stage, we monitor aging degradation. Our proposed method is based on the use of a one-class support vector machine and a normal support vector machine. The experimental results obtained in this study show that our proposed method can be employed to identify malfunction signs, which are different from normal-condition data, and to monitor aging degradation.