Sabhan Kanata, S. Suwarno, G. H. Sianipar, Nur Ulfa Maulidevi
{"title":"电力系统多目标无功优化调度问题的非支配排序遗传算法III","authors":"Sabhan Kanata, S. Suwarno, G. H. Sianipar, Nur Ulfa Maulidevi","doi":"10.1109/ICHVEPS47643.2019.9011144","DOIUrl":null,"url":null,"abstract":"The non-dominated sorting genetic algorithm (NSGA-III) was introduced to solve multi-objective optimal reactive power dispatch (MORPD) problems. MORPD as a non-linear, multi-objective optimization problem has the characteristics of non-convex, multi-constraint, and multi-variable (mix of discrete and continuous variables). The aim is to minimize the real power losses and voltage deviations. The feasibility of the proposed method was tested on the IEEE 57-bus power systems. The comparison of simulation results with the previous studies which applied the mixed variables of continuous and discrete showed that the proposed optimization method is more efficient and reliable in minimize the real power losses and computing period compared to multi-objective enhanced particle swarm optimization (MOEPSO), multi-objective particle swarm optimization (MOPSO) and multi-objective ant lion optimization (MOALO).","PeriodicalId":6677,"journal":{"name":"2019 2nd International Conference on High Voltage Engineering and Power Systems (ICHVEPS)","volume":"99 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Non-dominated Sorting Genetic Algorithm III for Multi-objective Optimal Reactive Power Dispatch Problem in Electrical Power System\",\"authors\":\"Sabhan Kanata, S. Suwarno, G. H. Sianipar, Nur Ulfa Maulidevi\",\"doi\":\"10.1109/ICHVEPS47643.2019.9011144\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The non-dominated sorting genetic algorithm (NSGA-III) was introduced to solve multi-objective optimal reactive power dispatch (MORPD) problems. MORPD as a non-linear, multi-objective optimization problem has the characteristics of non-convex, multi-constraint, and multi-variable (mix of discrete and continuous variables). The aim is to minimize the real power losses and voltage deviations. The feasibility of the proposed method was tested on the IEEE 57-bus power systems. The comparison of simulation results with the previous studies which applied the mixed variables of continuous and discrete showed that the proposed optimization method is more efficient and reliable in minimize the real power losses and computing period compared to multi-objective enhanced particle swarm optimization (MOEPSO), multi-objective particle swarm optimization (MOPSO) and multi-objective ant lion optimization (MOALO).\",\"PeriodicalId\":6677,\"journal\":{\"name\":\"2019 2nd International Conference on High Voltage Engineering and Power Systems (ICHVEPS)\",\"volume\":\"99 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 2nd International Conference on High Voltage Engineering and Power Systems (ICHVEPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICHVEPS47643.2019.9011144\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 2nd International Conference on High Voltage Engineering and Power Systems (ICHVEPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHVEPS47643.2019.9011144","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Non-dominated Sorting Genetic Algorithm III for Multi-objective Optimal Reactive Power Dispatch Problem in Electrical Power System
The non-dominated sorting genetic algorithm (NSGA-III) was introduced to solve multi-objective optimal reactive power dispatch (MORPD) problems. MORPD as a non-linear, multi-objective optimization problem has the characteristics of non-convex, multi-constraint, and multi-variable (mix of discrete and continuous variables). The aim is to minimize the real power losses and voltage deviations. The feasibility of the proposed method was tested on the IEEE 57-bus power systems. The comparison of simulation results with the previous studies which applied the mixed variables of continuous and discrete showed that the proposed optimization method is more efficient and reliable in minimize the real power losses and computing period compared to multi-objective enhanced particle swarm optimization (MOEPSO), multi-objective particle swarm optimization (MOPSO) and multi-objective ant lion optimization (MOALO).