A modified teaching-learning based optimization for the location and size of two SVCs to compensate the railway’s voltage drop

The recent concerns about fossil fuels have made mass transportations such as electric railways more popular than before. Meanwhile, traction loads are generally complex electrical loads that should be managed by the main electric grid when operated by the Railway Company. In this way, static VAr compensators (SVCs) is a precious tool for preserving the power quality of the electric grid in the presence of electric railways. Therefore, this paper discusses the locating of two SVCs in a rail way with modified teaching- learning based optimization (MTLBO). The results are compared with performing the optimization by Particle swarm optimization (PSO) algorithm.

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W artykule analizuje się metody optymalizacji położenia i rozmiaru statycznego kompensatora mocy biernej SVCs w sieci trakcyjnej. Do tego celu wykorzystuje się zmodyfikowaną metodę optymalizacji bazującej na algorytmie nauczania/uczenia się MTLBO.