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Loss Minimization of Electrified Railway Traction Systems Using SVC Based on Particle Swarm Optimization

In this paper, we propose a method to minimize the system losses of electrified railway traction systems by utilizing static var compensators (SVCs). We suggest a power flow analysis model for a railway system considering two types of SVC connections: line-to-ground and line-to-line connections. Furthermore, an optimal operation whose objective is to minimize the system loss is presented based on particle swarm optimization by utilizing the power flow calculation method. The proposed power flow model and optimal operation are verified using MATLAB and PSCAD simulation programs. An electrified railway traction system can be operated economically by exploiting the proposed method.
- Seoul National University Korea (Republic of)
- Chosun University Korea (Republic of)
- Electric Power Research Institute United States
- Chosun University Korea (Republic of)
- Seoul National University Korea (Republic of)
particle swarm optimization, power flow analysis, Auto-transformer, railway traction system, TK1-9971, static var compensator, Electrical engineering. Electronics. Nuclear engineering
particle swarm optimization, power flow analysis, Auto-transformer, railway traction system, TK1-9971, static var compensator, Electrical engineering. Electronics. Nuclear engineering
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).10 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
