{"title":"基于粒子群算法的直流牵引变电站电压控制在轨道交通系统中的最优能效","authors":"Waiard Saikong, Banri Khemkladmuk, Chaiyut Sumpavakup, Chanchai Techawatcharapaikul, Thanatchai Kulworawanichpong","doi":"10.1049/els2/5531109","DOIUrl":null,"url":null,"abstract":"<p>This article discusses voltage level modifications in urban mass transit traction substations, focusing on DC railway substations, to reduce power consumption and improve energy efficiency. Substation voltage settings are usually adjusted by a skilled designer using practical judgment and design acumen. To maximize operations, all traction substation voltage levels are automatically adjusted to the same value. This arrangement works well and may not have affected the power supply system. This design often causes operations to deviate from optimal performance, perhaps reducing energy efficiency. This research seeks to determine the optimal traction substation voltage setting that minimizes total energy consumption of DC electric railways. A simulation-based approach is applied using train movement data and voltage variation scenarios. The proposed designs are linear, <i>V</i>-shaped, and fixed-voltage. Additionally, particle swarm optimization (PSO) is an effective way to find the best design. The Bangkok Transit System (BTS) Sukhumvit line is used for testing. Reduction by the linear framework, energy consumption may be 2.341% lower than the base case. By the PSO, the results in 30 trial test runs suggest a 6.107% energy consumption reduction from baseline.</p>","PeriodicalId":48518,"journal":{"name":"IET Electrical Systems in Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/els2/5531109","citationCount":"0","resultStr":"{\"title\":\"Optimal Energy Efficiency in a Mass Rapid Transit System Through DC Traction Substation Voltage Control Utilizing Particle Swarm Optimization\",\"authors\":\"Waiard Saikong, Banri Khemkladmuk, Chaiyut Sumpavakup, Chanchai Techawatcharapaikul, Thanatchai Kulworawanichpong\",\"doi\":\"10.1049/els2/5531109\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This article discusses voltage level modifications in urban mass transit traction substations, focusing on DC railway substations, to reduce power consumption and improve energy efficiency. Substation voltage settings are usually adjusted by a skilled designer using practical judgment and design acumen. To maximize operations, all traction substation voltage levels are automatically adjusted to the same value. This arrangement works well and may not have affected the power supply system. This design often causes operations to deviate from optimal performance, perhaps reducing energy efficiency. This research seeks to determine the optimal traction substation voltage setting that minimizes total energy consumption of DC electric railways. A simulation-based approach is applied using train movement data and voltage variation scenarios. The proposed designs are linear, <i>V</i>-shaped, and fixed-voltage. Additionally, particle swarm optimization (PSO) is an effective way to find the best design. The Bangkok Transit System (BTS) Sukhumvit line is used for testing. Reduction by the linear framework, energy consumption may be 2.341% lower than the base case. By the PSO, the results in 30 trial test runs suggest a 6.107% energy consumption reduction from baseline.</p>\",\"PeriodicalId\":48518,\"journal\":{\"name\":\"IET Electrical Systems in Transportation\",\"volume\":\"2025 1\",\"pages\":\"\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2025-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/els2/5531109\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Electrical Systems in Transportation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/els2/5531109\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Electrical Systems in Transportation","FirstCategoryId":"5","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/els2/5531109","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Optimal Energy Efficiency in a Mass Rapid Transit System Through DC Traction Substation Voltage Control Utilizing Particle Swarm Optimization
This article discusses voltage level modifications in urban mass transit traction substations, focusing on DC railway substations, to reduce power consumption and improve energy efficiency. Substation voltage settings are usually adjusted by a skilled designer using practical judgment and design acumen. To maximize operations, all traction substation voltage levels are automatically adjusted to the same value. This arrangement works well and may not have affected the power supply system. This design often causes operations to deviate from optimal performance, perhaps reducing energy efficiency. This research seeks to determine the optimal traction substation voltage setting that minimizes total energy consumption of DC electric railways. A simulation-based approach is applied using train movement data and voltage variation scenarios. The proposed designs are linear, V-shaped, and fixed-voltage. Additionally, particle swarm optimization (PSO) is an effective way to find the best design. The Bangkok Transit System (BTS) Sukhumvit line is used for testing. Reduction by the linear framework, energy consumption may be 2.341% lower than the base case. By the PSO, the results in 30 trial test runs suggest a 6.107% energy consumption reduction from baseline.