Interactive Abnormal Condition Sign Discovery for Hydroelectric Power Plants

Abstract

Kyushu Electric Power Co.,Inc. collects various sensor data and weather information to maintain hydroelectric power plants while the plants are running. However, it is very rare to occur abnormal and trouble condition data in power equipments. And in order to collect the abnormal and trouble condition data, it is hard to construct an experimental hydroelectric power plant. Because its cost is very high. In this situation, we have to find abnormal condition data as a risk management. In this paper, we consider that the abnormal condition sign may be unusual condition data. This paper shows results of unusual condition data of bearing vibration detected from the collected various sensor data and weather information by using one class support vector machine. The result shows that our approach may be useful for unusual condition data detection and maintaining hydroelectric power plants. Therefore, the proposed method is one of risk management for hydroelectric power plants.