The current model of the Brazilian electric sector allows equal terms to all actors and reduces the role of the State in this sector. This model forces the electrical utilities to improve the quality of their products and, as a prerequisite for this purpose, they should make more effective use of the enormous amount of operational data that are stored in databases, acquired from the operation of their electrical systems which use the hydroelectric power plants as their main source of energy generation. One of the main tools for managing the operation of these plants are the Supervisory Control and Data Acquisition systems (SCADA). Thus, the large amount of data stored in databases by SCADA systems, certainly containing relevant information, should be treated to discover relationships and patterns that would help in the understanding of many important operational aspects as well as in the evaluation of operational performance of the electric power systems. The process of Knowledge Discovery in Database (KDD) is the process of identification of patterns in large data sets, that are valid, new, and useful to improve the understanding of a problem or a decision-making procedure. Data Mining is the step within KDD that extracts useful information from large databases. In this scenario, the present study objective is to perform data mining experiments on data generated by power plants SCADA systems, to produce relevant information to assist in planning, operation, maintenance and security of hydro power plants and also contribute to the implementation of the culture of using data mining techniques applied to these plants.
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