Karam Husen Khan, Khagendra Bahadur Thapa, Nava Raj Karki
{"title":"基于增强L-SHADE算法的定向过流继电器优化协调","authors":"Karam Husen Khan, Khagendra Bahadur Thapa, Nava Raj Karki","doi":"10.1109/POWERCON48463.2020.9230593","DOIUrl":null,"url":null,"abstract":"An optimization model based protection coordination method of directional overcurrent relays (DOCRs) in highly interconnected large size meshed power system network is a complex task due to its nature of highly constrained non-linear optimization problem. To overcome this complexity of DOCRs coordination, this paper proposes an optimal protection coordination of DOCRs considering optimal selection of standard time-current characteristic curves using enhanced linear population size reduction technique of success history based adaptive differential evolution (enhanced L-SHADE/eL-SHADE) algorithm which is an advanced version of differential evolution (DE) algorithm. The conventional L-SHADE algorithm is enhanced in three steps with a novel mutation strategy, incorporation of random local search by sequential quadratic programming and non-linear population size reduction scheme. Furthermore, an effectiveness of the proposed algorithm is validated by testing it on standard IEEE-30 bus and 57 bus meshed networks. A promising and highly competitive simulation results are obtained when compared with similar results presented in reference articles by other optimization algorithms. The evaluation criteria of the eL-SHADE algorithm is considered on the basis of objective function value, standard deviation, execution time and violation of constraints.","PeriodicalId":306418,"journal":{"name":"2020 IEEE International Conference on Power Systems Technology (POWERCON)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Optimal Coordination of Directional Overcurrent Relays Using Enhanced L-SHADE Algorithm\",\"authors\":\"Karam Husen Khan, Khagendra Bahadur Thapa, Nava Raj Karki\",\"doi\":\"10.1109/POWERCON48463.2020.9230593\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An optimization model based protection coordination method of directional overcurrent relays (DOCRs) in highly interconnected large size meshed power system network is a complex task due to its nature of highly constrained non-linear optimization problem. To overcome this complexity of DOCRs coordination, this paper proposes an optimal protection coordination of DOCRs considering optimal selection of standard time-current characteristic curves using enhanced linear population size reduction technique of success history based adaptive differential evolution (enhanced L-SHADE/eL-SHADE) algorithm which is an advanced version of differential evolution (DE) algorithm. The conventional L-SHADE algorithm is enhanced in three steps with a novel mutation strategy, incorporation of random local search by sequential quadratic programming and non-linear population size reduction scheme. Furthermore, an effectiveness of the proposed algorithm is validated by testing it on standard IEEE-30 bus and 57 bus meshed networks. A promising and highly competitive simulation results are obtained when compared with similar results presented in reference articles by other optimization algorithms. The evaluation criteria of the eL-SHADE algorithm is considered on the basis of objective function value, standard deviation, execution time and violation of constraints.\",\"PeriodicalId\":306418,\"journal\":{\"name\":\"2020 IEEE International Conference on Power Systems Technology (POWERCON)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Power Systems Technology (POWERCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/POWERCON48463.2020.9230593\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Power Systems Technology (POWERCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/POWERCON48463.2020.9230593","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal Coordination of Directional Overcurrent Relays Using Enhanced L-SHADE Algorithm
An optimization model based protection coordination method of directional overcurrent relays (DOCRs) in highly interconnected large size meshed power system network is a complex task due to its nature of highly constrained non-linear optimization problem. To overcome this complexity of DOCRs coordination, this paper proposes an optimal protection coordination of DOCRs considering optimal selection of standard time-current characteristic curves using enhanced linear population size reduction technique of success history based adaptive differential evolution (enhanced L-SHADE/eL-SHADE) algorithm which is an advanced version of differential evolution (DE) algorithm. The conventional L-SHADE algorithm is enhanced in three steps with a novel mutation strategy, incorporation of random local search by sequential quadratic programming and non-linear population size reduction scheme. Furthermore, an effectiveness of the proposed algorithm is validated by testing it on standard IEEE-30 bus and 57 bus meshed networks. A promising and highly competitive simulation results are obtained when compared with similar results presented in reference articles by other optimization algorithms. The evaluation criteria of the eL-SHADE algorithm is considered on the basis of objective function value, standard deviation, execution time and violation of constraints.