Application of Classifier System and Co-Evolutionary Algorithm in Optimization of Medium-Voltage Distribution Networks Post-Fault Configuration

摘要

During the operation of power electric distribution network there are disturbances occurring in its elements. The power failures and delivery limitations depend on the network operation system configuration, and the way and time of recovery depend on system configuration, distribution equipment and network automatics [1-3]. The analysed problem of choosing the substitute configuration of the distribution system can be described as a multiobjective programming problem. This article describes the development of this type of calculation methods, simultaneously containing their own innovative solution proposals concerning the application of a classification system working with the co-evolutionary algorithm. In works [4-10] are presented methods concerning the use revolution algorithms drawn up to resolve multi-criteria problems in optimising electric power networks. Heuristic search algorithms - their use is hindered in the event of calculations for large numbers of current network nodes in the analysed network. In such instances assumptions may be applied limiting the extent of solutions, which reduces the calculation process, but in consequence causes the search for sub-optimal solutions. Genetic and evolutionary algorithms - the benefit of application of evolutionary algorithms is the possibility of their use in large numbers of decisively variable decisions and also complex descriptions of function purpose and limiting conditions. Cooperation of the co-evolutionary algorithm with the classification system (drawn up by the author of the work) enables significant reduction of the classification time (reduces the iterative calculation process on average by 40%), which is significant from the practical point of view in the application of this method in current systems of distribution network operation management. The application of a classification system to the analysed task also enables improvement of the effectiveness of the performance process of designating the scenario of the substitute network configurations. Improvement of the efficiency of the network configuration designation process is obtained using the sought information (with use of the announcement creation process), in the collections of classifiers to create sub-populations of solutions for the co-evolutionary algorithm, which would be used to search for the collection of Pareto-optimal solutions. The process of creating a collection of classifiers describing the substitute network configuration was performed by the author supported by the theoretical genetic basics of self-teaching system. Classifiers may be created (for analysed network structure) for the most probable break down situations, which arise from regarding the stage of choice of the simulated break down situations reliability characteristics and the usage durations of network elements.