A significant amount of electric energy is produced applying regenerative braking in railway systems. The optimum use of this energy can improve the energy consumption efficiency in a railway Traction Power Supply Substation (TPSS). This paper presents a new method restoring the Regenerative Braking Energy (RBE) to the distribution grid and converting TPSS as a microgrid. In the proposed strategy, a Railway Power Quality Compensator (RPQC) exerted to feeding back RBE to the grid compliant with IEEE Power Quality (PQ) standards. Moreover, the proposed topology can supply other adjacent running trains. The intelligent control system used for RPQC can suppress harmonics, dominate Negative Sequence Current (NSC) and compensate Power Factor (PF). The simulation results have been provided to verify the effectiveness of the proposed strategy.

An intelligent strategy for regenerative braking energy harvesting in AC electrical railway substation

Kaleybar, Hamed Jafari;Brenna, Morris;Foiadelli, Federica;Zaninelli, Dario
2017-01-01

Abstract

A significant amount of electric energy is produced applying regenerative braking in railway systems. The optimum use of this energy can improve the energy consumption efficiency in a railway Traction Power Supply Substation (TPSS). This paper presents a new method restoring the Regenerative Braking Energy (RBE) to the distribution grid and converting TPSS as a microgrid. In the proposed strategy, a Railway Power Quality Compensator (RPQC) exerted to feeding back RBE to the grid compliant with IEEE Power Quality (PQ) standards. Moreover, the proposed topology can supply other adjacent running trains. The intelligent control system used for RPQC can suppress harmonics, dominate Negative Sequence Current (NSC) and compensate Power Factor (PF). The simulation results have been provided to verify the effectiveness of the proposed strategy.
2017
5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - Proceedings
9781509064847
electrical railway; energy saving; Intelligent control; power quality; regenerative braking energy; Modeling and Simulation; Transportation; Computer Networks and Communications; Artificial Intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1038242
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