{"title":"结合降维潮流法的差分进化配电网电容器优化配置","authors":"S. Neelima, Dr. P. S. Subramanyam","doi":"10.1109/ISET-INDIA.2011.6145350","DOIUrl":null,"url":null,"abstract":"A distribution system is an interface between the bulk power system and the consumers. Among these systems, radial distributions system is popular because of low cost and simple design. In distribution systems, the voltages at buses decrease proportionally, when moved away from the substation, also the losses increases quadraticaly. The reason for decrease in voltage and increase in losses is the insufficient amount of reactive power, which can be provided by the shunt capacitors. But the placement of the capacitor with appropriate size is always a challenge. Thus the optimal capacitor placement problem is to determine the location and size of capacitors to be placed in distribution networks in an efficient way to reduce the power losses and improve the voltage profile of the system. For this purpose, in this paper, two stage methodologies are used. In first stage, the load flow of pre-compensated distribution system is carried out using ‘dimension reducing distribution load flow algorithm (DRDLFA)’. On the basis of this load flow the potential locations of compensation are computed. In the second stage, Differential Evolution (DE) Algorithm is used to determine the optimal location and size of the capacitors such that the cost of the energy loss and capacitor cost to be a minimum. The above method is tested on IEEE 69 bus system and compared with other methods in the literature like Particle swarm optimization.","PeriodicalId":265646,"journal":{"name":"ISGT2011-India","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Optimal capacitor placement in distribution networks using differential evolution incorporating dimension reducing power flow method\",\"authors\":\"S. Neelima, Dr. P. S. Subramanyam\",\"doi\":\"10.1109/ISET-INDIA.2011.6145350\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A distribution system is an interface between the bulk power system and the consumers. Among these systems, radial distributions system is popular because of low cost and simple design. In distribution systems, the voltages at buses decrease proportionally, when moved away from the substation, also the losses increases quadraticaly. The reason for decrease in voltage and increase in losses is the insufficient amount of reactive power, which can be provided by the shunt capacitors. But the placement of the capacitor with appropriate size is always a challenge. Thus the optimal capacitor placement problem is to determine the location and size of capacitors to be placed in distribution networks in an efficient way to reduce the power losses and improve the voltage profile of the system. For this purpose, in this paper, two stage methodologies are used. In first stage, the load flow of pre-compensated distribution system is carried out using ‘dimension reducing distribution load flow algorithm (DRDLFA)’. On the basis of this load flow the potential locations of compensation are computed. In the second stage, Differential Evolution (DE) Algorithm is used to determine the optimal location and size of the capacitors such that the cost of the energy loss and capacitor cost to be a minimum. The above method is tested on IEEE 69 bus system and compared with other methods in the literature like Particle swarm optimization.\",\"PeriodicalId\":265646,\"journal\":{\"name\":\"ISGT2011-India\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISGT2011-India\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISET-INDIA.2011.6145350\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISGT2011-India","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISET-INDIA.2011.6145350","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal capacitor placement in distribution networks using differential evolution incorporating dimension reducing power flow method
A distribution system is an interface between the bulk power system and the consumers. Among these systems, radial distributions system is popular because of low cost and simple design. In distribution systems, the voltages at buses decrease proportionally, when moved away from the substation, also the losses increases quadraticaly. The reason for decrease in voltage and increase in losses is the insufficient amount of reactive power, which can be provided by the shunt capacitors. But the placement of the capacitor with appropriate size is always a challenge. Thus the optimal capacitor placement problem is to determine the location and size of capacitors to be placed in distribution networks in an efficient way to reduce the power losses and improve the voltage profile of the system. For this purpose, in this paper, two stage methodologies are used. In first stage, the load flow of pre-compensated distribution system is carried out using ‘dimension reducing distribution load flow algorithm (DRDLFA)’. On the basis of this load flow the potential locations of compensation are computed. In the second stage, Differential Evolution (DE) Algorithm is used to determine the optimal location and size of the capacitors such that the cost of the energy loss and capacitor cost to be a minimum. The above method is tested on IEEE 69 bus system and compared with other methods in the literature like Particle swarm optimization.