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EN
Reconstructing power systems has changed the traditional planning of power systems and has raised new challenges in Transmission Expansion Planning (TEP). Because of these reason, new methods and criteria have been formed for planning transmission in reconstructed environments. Thus, a dynamic programming was used for transmission efficiency based on multi-objective optimization in this research. In this model, investment cost, cost of density and dependability have been considered three objectives of optimization. In this paper, NSGAII multi-objective genetic algorithm was used to solve this non-convex and mixed integer problem. A fuzzy decision method has been used to choose the final optimal answer from the Pareto solutions obtained from NSGAII. Moreover, to confirm the efficiency of NSGAII multi-objective genetic algorithm in solving TEP problem, this algorithm was implemented in an IEEE 24 bus system and the gained results were compared with previous works in this field.
EN
The reported diagnosis supporting system was provided with knowledge base determined by the disease characteristic features descriptors that were recorded in conclusions table. Every descriptor defines the elementary rules related to every disease factor threshold value, recognised as a sign of the disease presence (the over-gone physiological state). The introduced definitions of the disease characteristics and some fuzzy logic proposals implementations were defined for the decision making system development.
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