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Distributed Reactive Power Compensation Method in DC Traction Power Systems With Reversible Substations

Fengjie Hao, Gang Zhang, Jie Chen, Zhigang Liu

2021IEEE Transactions on Vehicular Technology26 citationsDOI

Abstract

This paper proposes a potential distributed reactive power compensation method in dc traction power systems with reversible substations. It utilizes the entire capacity of reversible substations during night hours and the reminding capacity except for regenerative braking energy feedback during day hours. A unified dispatch scheme is developed to allocate reactive power compensation capacity for each reversible substation. During night hours the entire capacity is used to accomplish the optimal power flow objective. During day hours the excess capacity after active power production is used to improve the system reactive power factor dynamically. The different objectives throughout the day are implemented in a unified formula which avoids manually change. In addition the outage of reversible substations and the low dc voltage conditions are considered and an over-modulation suppression strategy is included in the unified dispatch scheme to adjust the reactive power allocation among reversible substations. This novel utilization method of reversible substations is able to provide significant improvements in the power factor of dc traction power systems which would otherwise require expensive additional equipment such as static synchronous compensator. Finally the effectiveness of the proposed method is verified with MATLAB/SIMULINK.

Topics & Concepts

AC powerPower factorCompensation (psychology)Electric power systemEngineeringMATLABControl theory (sociology)Power (physics)Traction power networkVoltageTraction (geology)Computer scienceElectronic engineeringElectrical engineeringControl (management)PsychoanalysisOperating systemArtificial intelligencePhysicsMechanical engineeringPsychologyQuantum mechanicsRailway Systems and Energy EfficiencyHVDC Systems and Fault ProtectionMicrogrid Control and Optimization
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