{"title":"基于化学反应优化技术的单机电力系统稳定器优化设计","authors":"S. Paul, P. Roy","doi":"10.4018/IJEOE.2015040104","DOIUrl":null,"url":null,"abstract":"PSSs are added to excitation systems to enhance the damping during low frequency oscillations. The non-linear model of a machine is linearized at different operating points. Chemical Reaction optimization (CRO), a new population based search algorithm is been proposed in this paper to damp the power system low-frequency oscillations and enhance power system stability. Computation results demonstrate that the proposed algorithm is effective in damping low frequency oscillations as well as improving system dynamic stability. The performance of the proposed algorithm is evaluated for different loading conditions. In addition, the proposed algorithm is more effective and provides superior performance when compared other population based optimization algorithms like differential evolution (DE) and particle swarm optimization (PSO).","PeriodicalId":246250,"journal":{"name":"Int. J. Energy Optim. Eng.","volume":"64 6","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Optimal Design of Single Machine Power System Stabilizer using Chemical Reaction Optimization Technique\",\"authors\":\"S. Paul, P. Roy\",\"doi\":\"10.4018/IJEOE.2015040104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PSSs are added to excitation systems to enhance the damping during low frequency oscillations. The non-linear model of a machine is linearized at different operating points. Chemical Reaction optimization (CRO), a new population based search algorithm is been proposed in this paper to damp the power system low-frequency oscillations and enhance power system stability. Computation results demonstrate that the proposed algorithm is effective in damping low frequency oscillations as well as improving system dynamic stability. The performance of the proposed algorithm is evaluated for different loading conditions. In addition, the proposed algorithm is more effective and provides superior performance when compared other population based optimization algorithms like differential evolution (DE) and particle swarm optimization (PSO).\",\"PeriodicalId\":246250,\"journal\":{\"name\":\"Int. J. Energy Optim. Eng.\",\"volume\":\"64 6\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Energy Optim. Eng.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/IJEOE.2015040104\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Energy Optim. Eng.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJEOE.2015040104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal Design of Single Machine Power System Stabilizer using Chemical Reaction Optimization Technique
PSSs are added to excitation systems to enhance the damping during low frequency oscillations. The non-linear model of a machine is linearized at different operating points. Chemical Reaction optimization (CRO), a new population based search algorithm is been proposed in this paper to damp the power system low-frequency oscillations and enhance power system stability. Computation results demonstrate that the proposed algorithm is effective in damping low frequency oscillations as well as improving system dynamic stability. The performance of the proposed algorithm is evaluated for different loading conditions. In addition, the proposed algorithm is more effective and provides superior performance when compared other population based optimization algorithms like differential evolution (DE) and particle swarm optimization (PSO).