Zhang, Jian, Liu, Wei, Tian, Zhongbei 
ORCID: 0000-0001-7295-3327, Qi, He, Zeng, Jiaxin and Yang, Yuheng
(2022)
Urban Rail Substation Parameter Optimization by Energy Audit and Modified Salp Swarm Algorithm.
IEEE TRANSACTIONS ON POWER DELIVERY, 37 (6).
pp. 4968-4978.
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220329Urban Rail Substation Parameter Optimization by Energy Audit and Modified Salp Swarm Algorithm.pdf - Author Accepted Manuscript Download (850kB) | Preview |
Abstract
Auditing the energy consumption in urban rail is vital for energy consumption evaluation and system parameter design. There are multiple ways to audit energy consumption, but a universal and global approach is missing. The system-level traction energy consumption (STEC) is proposed. Compared to the main substation energy consumption (MSEC), STEC is more accurate by eliminating the influence of step-down loads based on field test studies. An optimization parameter designing model is built, which takes the system cost as the optimal object considering the life span of energy feedback system (EFS)s. The modified salp swarm algorithm (MSSA) is proposed as the optimization algorithm. The numerical tests show that MSSA has better converge performance than salp swarm algorithm (SSA) and particle swarm optimization (PSO). The impact factors of STEC are analyzed. Compared with SSA and PSO, the initial value of the MSSA is improved and it evolves faster. Compared with the case that does not take the no-load voltage of rectifiers and start voltage of EFSs as optimal parameters, the composite cost of the case that takes the abovementioned parameters as optimal parameters is 3.49% less. Compared to the system without EFSs, the optimal system with EFSs can save costs by 29.47%.
| Item Type: | Article |
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| Uncontrolled Keywords: | Energy audit, modified salp swarm algorithm, optimization parameter designing |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 06 Apr 2022 13:14 |
| Last Modified: | 15 Mar 2024 17:23 |
| DOI: | 10.1109/TPWRD.2022.3164408 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3152274 |
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