A data mining experiment on a SCADA system’s historical acquired data

In the SCADA system implemented for a hydroelectric power plants cascade, monitoring, control and over-limit alarm processes provide large amounts of historical data stored in distributed databases. In their preliminary form these data don’t offer a performance knowledge discovery. Intelligent data mining framework applied over historical acquired data is used to extract hidden information directly from the SCADA system’s database records. This paper presents a data mining and visualization experiment performed on a real data and results achieved in the experiment. The decision trees algorithm is used in order to find patterns in historical data and to establish a data mining flow.