{"title":"灰狼优化算法在电力系统稳定性增强中的实现与评价","authors":"A. Alahmed, Salman U. Taiwo, M. A. Abido","doi":"10.1109/GCC45510.2019.1570512680","DOIUrl":null,"url":null,"abstract":"The increasing complexity of today’s applications has surfaced the importance of meta-heuristic techniques which can deal with multi-variable, multi-constraints, highly non-linear and non-smooth problems. Their superior performance and immunity of getting trapped in local maxima or minima made them eminent when compared with classical optimization methods, which have several limitations. In this context, implementation and evaluation of Grey Wolf optimization Algorithm (GWOA) on power system stability enhancement will be carried. The objective function is to maximize the minimum damping ratio of the controller to enhance stability and ensure faster damping. The results will be then compared with other evolutionary techniques, particularly Real-coded Genetic Algorithm (RCGA) and Differential Evolution (DE) method. The simulation results will be established using MATLAB.","PeriodicalId":352754,"journal":{"name":"2019 IEEE 10th GCC Conference & Exhibition (GCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Implementation and Evaluation of Grey Wolf optimization Algorithm on Power System Stability Enhancement\",\"authors\":\"A. Alahmed, Salman U. Taiwo, M. A. Abido\",\"doi\":\"10.1109/GCC45510.2019.1570512680\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The increasing complexity of today’s applications has surfaced the importance of meta-heuristic techniques which can deal with multi-variable, multi-constraints, highly non-linear and non-smooth problems. Their superior performance and immunity of getting trapped in local maxima or minima made them eminent when compared with classical optimization methods, which have several limitations. In this context, implementation and evaluation of Grey Wolf optimization Algorithm (GWOA) on power system stability enhancement will be carried. The objective function is to maximize the minimum damping ratio of the controller to enhance stability and ensure faster damping. The results will be then compared with other evolutionary techniques, particularly Real-coded Genetic Algorithm (RCGA) and Differential Evolution (DE) method. The simulation results will be established using MATLAB.\",\"PeriodicalId\":352754,\"journal\":{\"name\":\"2019 IEEE 10th GCC Conference & Exhibition (GCC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 10th GCC Conference & Exhibition (GCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GCC45510.2019.1570512680\",\"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 IEEE 10th GCC Conference & Exhibition (GCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCC45510.2019.1570512680","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Implementation and Evaluation of Grey Wolf optimization Algorithm on Power System Stability Enhancement
The increasing complexity of today’s applications has surfaced the importance of meta-heuristic techniques which can deal with multi-variable, multi-constraints, highly non-linear and non-smooth problems. Their superior performance and immunity of getting trapped in local maxima or minima made them eminent when compared with classical optimization methods, which have several limitations. In this context, implementation and evaluation of Grey Wolf optimization Algorithm (GWOA) on power system stability enhancement will be carried. The objective function is to maximize the minimum damping ratio of the controller to enhance stability and ensure faster damping. The results will be then compared with other evolutionary techniques, particularly Real-coded Genetic Algorithm (RCGA) and Differential Evolution (DE) method. The simulation results will be established using MATLAB.