| dc.description.abstract | The reliable supply of electricity plays a key role in the contemporary way of life. In order to provide more benefits to the population, electrical networks are getting bigger to produce more energy. This growth, while necessary, brings problems in operation and maintenance, since the networks are more complex. This complexity requires that network security analysis should be performed in real time to avoid decision errors when disconnecting a device from the network or predicting the possibility of operating output from an undersized device. In this paper, an intelligent system for contingency selection in static security analysis of electric power systems is proposed. The indication of the contingencies is the first step to develop control action and maintain the system operation integrity. For this, contingency selection was modeled as a combinatorial optimization problem, employing an Ant Colony Optimization (ACO) meta-heuristic to indicate the most serious contingencies of the network under analysis. This approach adds to the state of the art, since no mention was made of the use of this model in the researched scientific circles. The proposed model differs from those found so far, since the search engine for new solutions was developed taking into account the vertices of the electrical network instead of the edges, in this work the edges are treated indirectly as a second step of the search for solutions. The developed system is evaluated using an IEEE (Institute of Electrical and Electronic Engineers) test network of 30 buses and a real network of 810 buses, considering double-branch contingencies. The results show an accuracy of 97.11% in the search for the most severe contingencies of the real network and 95.99% for the IEEE 30 busbar test network. The result found for the real network still presents the differential of a search space less traveled than those presented in other works. | en |
