{"title":"开关磁阻发电机控制的蚁群优化算法与模糊逻辑","authors":"Rabyi Tarik, B. Adil","doi":"10.3934/energy.2022045","DOIUrl":null,"url":null,"abstract":"This article discusses two methods to control the output voltage of switched reluctance generators (SRGs) used in wind generator systems. To reduce the ripple of the SRG output voltage, a closed-loop voltage control technique has been designed. In the first method, a proportional-integral (PI) controller is used. The parameters of the PI controller are tuned based on the voltage variation. The SRG is generally characterized by strong nonlinearities. However, finding appropriate values for the PI controller is not an easy task. To overcome this problem and simplify the process of tuning the PI controller parameters, a solution based on the ant colony optimization algorithm (ACO) was developed. To settle the PI parameters, several cost functions are used in the implementation of the ACO algorithm. To control the SRG output voltage, a second method was developed based on the fuzzy logic controller. Unlike several previous works, the proposed methods, ACO and fuzzy logic control, are easy to implement and can solve numerous optimization problems. To check the best approach, a comparison between the two methods was performed. Finally, to show the effectiveness of this study, we present examples of simulations that entail the use of a three-phase SRG with a 12/8 structure and SIMULINK tools.","PeriodicalId":45696,"journal":{"name":"AIMS Energy","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Ant colony optimization algorithm and fuzzy logic for switched reluctance generator control\",\"authors\":\"Rabyi Tarik, B. Adil\",\"doi\":\"10.3934/energy.2022045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article discusses two methods to control the output voltage of switched reluctance generators (SRGs) used in wind generator systems. To reduce the ripple of the SRG output voltage, a closed-loop voltage control technique has been designed. In the first method, a proportional-integral (PI) controller is used. The parameters of the PI controller are tuned based on the voltage variation. The SRG is generally characterized by strong nonlinearities. However, finding appropriate values for the PI controller is not an easy task. To overcome this problem and simplify the process of tuning the PI controller parameters, a solution based on the ant colony optimization algorithm (ACO) was developed. To settle the PI parameters, several cost functions are used in the implementation of the ACO algorithm. To control the SRG output voltage, a second method was developed based on the fuzzy logic controller. Unlike several previous works, the proposed methods, ACO and fuzzy logic control, are easy to implement and can solve numerous optimization problems. To check the best approach, a comparison between the two methods was performed. Finally, to show the effectiveness of this study, we present examples of simulations that entail the use of a three-phase SRG with a 12/8 structure and SIMULINK tools.\",\"PeriodicalId\":45696,\"journal\":{\"name\":\"AIMS Energy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AIMS Energy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3934/energy.2022045\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AIMS Energy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3934/energy.2022045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Ant colony optimization algorithm and fuzzy logic for switched reluctance generator control
This article discusses two methods to control the output voltage of switched reluctance generators (SRGs) used in wind generator systems. To reduce the ripple of the SRG output voltage, a closed-loop voltage control technique has been designed. In the first method, a proportional-integral (PI) controller is used. The parameters of the PI controller are tuned based on the voltage variation. The SRG is generally characterized by strong nonlinearities. However, finding appropriate values for the PI controller is not an easy task. To overcome this problem and simplify the process of tuning the PI controller parameters, a solution based on the ant colony optimization algorithm (ACO) was developed. To settle the PI parameters, several cost functions are used in the implementation of the ACO algorithm. To control the SRG output voltage, a second method was developed based on the fuzzy logic controller. Unlike several previous works, the proposed methods, ACO and fuzzy logic control, are easy to implement and can solve numerous optimization problems. To check the best approach, a comparison between the two methods was performed. Finally, to show the effectiveness of this study, we present examples of simulations that entail the use of a three-phase SRG with a 12/8 structure and SIMULINK tools.
期刊介绍:
AIMS Energy is an international Open Access journal devoted to publishing peer-reviewed, high quality, original papers in the field of Energy technology and science. We publish the following article types: original research articles, reviews, editorials, letters, and conference reports. AIMS Energy welcomes, but not limited to, the papers from the following topics: · Alternative energy · Bioenergy · Biofuel · Energy conversion · Energy conservation · Energy transformation · Future energy development · Green energy · Power harvesting · Renewable energy