Rough Set Theory: Data Mining Technique Applied to the Electrical Power System
This paper presents a study were the Rough Set Theory and Data Mining Technique are applied to the electrical power system. The Data Mining technique classifies the system operation in four possible states: normal, alert, emergency (emergency I and emerge
- PDF / 244,793 Bytes
- 10 Pages / 439.37 x 666.142 pts Page_size
- 20 Downloads / 243 Views
Rough Set Theory: Data Mining Technique Applied to the Electrical Power System C.I. Faustino Agreira, C.M. Machado Ferreira, and F.P. Maciel Barbosa
Abstract This paper presents a study were the Rough Set Theory and Data Mining Technique are applied to the electrical power system. The Data Mining technique classifies the system operation in four possible states: normal, alert, emergency (emergency I and emergency II). The states, that correspond to the normal state can be classified as secure and insecure the remaining ones. In this security studies, the overloads in transmition lines and the violation of the voltage limits are used to classify and rank these contingencies. This technique was applied to the 118IEEE busbar test power network and the results obtained are analyzed. Finally, some conclusions that provide a valuable contribution to the understanding of the power system security analysis are pointed out.
36.1
Introduction
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. This technique analyzes data from many different dimensions or angles, categorize it and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns
C.I.F. Agreira (*) Departamento de Engenharia Electrote´cnica, Instituto Superior de Engenharia de Coimbra, Coimbra, Portugal e-mail: [email protected] C.M.M. Ferreira Departamento de Engenharia Electrote´cnica, Instituto Superior de Engenharia de Coimbra, Coimbra, Portugal INESC, Coimbra, Portugal F.P.M. Barbosa INESC Tech, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal e-mail: [email protected] A. Madureira et al. (eds.), Computational Intelligence and Decision Making: Trends and 387 Applications, Intelligent Systems, Control and Automation: Science and Engineering 61, DOI 10.1007/978-94-007-4722-7_36, # Springer Science+Business Media Dordrecht 2013
388
C.I.F. Agreira et al.
among dozens of fields in large relational databases [1]. Many applications of knowledge discovery and data mining such as rule discovery for semantic query optimization, database integration and decision support, require the knowledge to be consistent with the data [2]. Data mining is an emerging area of computational intelligence that offers new theories, techniques and tools to processes large volumes of data. It has gained considerable attention among practitioners and researchers as evidenced by the number of publications, conferences, and application reports [3]. The growing volume of data that is available in a digital form has accelerated this interest. Data mining has emerged as a contribute tools for data analysis, discovery of new knowledge, and autonomous decision making [3]. The knowledge acquisition process is a complex task, since the experts have difficulty to explain how to solve a specified problem. Recently, the Rough Sets theory (RST) has been used successfully to handle efficiently problems where large amounts of data are produced [4, 5]. RST constitutes a fra
Data Loading...
