{"title":"基于进化规划技术的潮流可解性识别与计算算法","authors":"K. M. Talib, I. Musirin, M. Kalil, M. Idris","doi":"10.1109/SCORED.2007.4451445","DOIUrl":null,"url":null,"abstract":"Power flow study has been identified as the most important issue in power systems especially in the field of assessing the power system operability, survivability and also its security. It is considered as the back bone prior to further power system analysis, operation and planning. There have been various ready made products for power flow study packages in the market. Nonetheless, any attempts to solve power flow solution utilizing new developed algorithm or techniques can be considered as a brave trial. This paper proposes the Evolutionary Programming (EP) optimization technique to address the power flow problems through optimization technique. EP is based on the survivors of the fittest technique; where it is a sub-division of evolutionary computation (EC) under the hierarchy of Artificial Intelligence (AI). Its capability in solving multi-variables, non- convex, non-linear and/or single or mufti-objective optimization problems have been highlighted as the strength of EP. In realizing the effectiveness of EP in solving power problems, standard test system was utilized to ensure its workability in solving non-linear equations involving several pre-determined equality and inequality constraints equations. Results obtained from this study were compared with the existing established techniques; promising results were discovered implying that this technique is feasible to be implemented in addressing further optimization problems.","PeriodicalId":443652,"journal":{"name":"2007 5th Student Conference on Research and Development","volume":"132 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Power Flow Solvability Identification and Calculation Algorithm Using Evolutionary Programming Technique\",\"authors\":\"K. M. Talib, I. Musirin, M. Kalil, M. Idris\",\"doi\":\"10.1109/SCORED.2007.4451445\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Power flow study has been identified as the most important issue in power systems especially in the field of assessing the power system operability, survivability and also its security. It is considered as the back bone prior to further power system analysis, operation and planning. There have been various ready made products for power flow study packages in the market. Nonetheless, any attempts to solve power flow solution utilizing new developed algorithm or techniques can be considered as a brave trial. This paper proposes the Evolutionary Programming (EP) optimization technique to address the power flow problems through optimization technique. EP is based on the survivors of the fittest technique; where it is a sub-division of evolutionary computation (EC) under the hierarchy of Artificial Intelligence (AI). Its capability in solving multi-variables, non- convex, non-linear and/or single or mufti-objective optimization problems have been highlighted as the strength of EP. In realizing the effectiveness of EP in solving power problems, standard test system was utilized to ensure its workability in solving non-linear equations involving several pre-determined equality and inequality constraints equations. Results obtained from this study were compared with the existing established techniques; promising results were discovered implying that this technique is feasible to be implemented in addressing further optimization problems.\",\"PeriodicalId\":443652,\"journal\":{\"name\":\"2007 5th Student Conference on Research and Development\",\"volume\":\"132 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 5th Student Conference on Research and Development\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCORED.2007.4451445\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 5th Student Conference on Research and Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCORED.2007.4451445","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Power Flow Solvability Identification and Calculation Algorithm Using Evolutionary Programming Technique
Power flow study has been identified as the most important issue in power systems especially in the field of assessing the power system operability, survivability and also its security. It is considered as the back bone prior to further power system analysis, operation and planning. There have been various ready made products for power flow study packages in the market. Nonetheless, any attempts to solve power flow solution utilizing new developed algorithm or techniques can be considered as a brave trial. This paper proposes the Evolutionary Programming (EP) optimization technique to address the power flow problems through optimization technique. EP is based on the survivors of the fittest technique; where it is a sub-division of evolutionary computation (EC) under the hierarchy of Artificial Intelligence (AI). Its capability in solving multi-variables, non- convex, non-linear and/or single or mufti-objective optimization problems have been highlighted as the strength of EP. In realizing the effectiveness of EP in solving power problems, standard test system was utilized to ensure its workability in solving non-linear equations involving several pre-determined equality and inequality constraints equations. Results obtained from this study were compared with the existing established techniques; promising results were discovered implying that this technique is feasible to be implemented in addressing further optimization problems.