Optimization of Algorithm for Solving Railroad Power Conditioner Compensation Power Reference Value and System Power Quality Analysis Based on Optimal Compensation Model

Can Ding, Yuejin Guo (), Haichuan You and Hongrong Zhang
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Can Ding: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443000, China
Yuejin Guo: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443000, China
Haichuan You: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443000, China
Hongrong Zhang: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443000, China

Energies, 2023, vol. 16, issue 20, 1-21

Abstract: At present, electrified railroads with complex road conditions are facing the problems of the existence of a single power supply method, the deterioration in power quality, and the difficulty in recycling regenerative braking energy. In order to improve the above problems, this paper establishes a traction power supply system containing photovoltaic units, proposes an optimized compensation model for the RPC (railroad power conditioner), and improves the particle swarm algorithm for solving the reference value of RPC compensation power. First, the structure of the RPC-based traction photovoltaic power generation system and the establishment of the integrated energy management strategy of the system are constructed. Then, the back-to-back converter compensation model with power quality index parameter constraints is established, which establishes the optimization function with the objectives of minimizing the negative sequence current, maximizing the power factor, and minimizing the RPC compensation power, as well as establishes constraints on the active converter capacity and three-phase voltage imbalance. Then, the particle swarm algorithm for solving the RPC compensation power reference value is improved, specifically in the original PSO by introducing dynamically changing inertia weights and learning factors. This not only solves the problem of the single power supply and realizes the nearby consumption of photovoltaic units in the traction system, but also realizes the recycling of regenerative braking energy and the coordinated control of the traction photovoltaic power generation system, improves the power quality of the system, and meets the demand of the RPC for real-time control. In order to verify the effectiveness of the optimized compensation model established in this paper and improve the convergence of the particle swarm algorithm for solving the RPC compensation power reference value, a simulation model of the traction PV power generation system is established in MATLAB/Simulink, and a real-time simulation is carried out to verify it based on the preset working condition data. The results show that the RPC optimization compensation model developed in this paper can coordinate the control system energy flow and improve the system power quality (the power factor increases and the negative sequence current decreases). The improved particle swarm algorithm is more convergent.

Keywords: photovoltaic power generation system for traction; railroad power conditioner; optimized compensation model; particle swarm algorithm; power quality; energy flow; power factor; negative sequence current (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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