{"title":"并网和离网PV-WT系统高效存储单元和EP-ANFIS控制器设计","authors":"S. V. Karemore, Eda Vijay Kumar","doi":"10.3311/ppee.20364","DOIUrl":null,"url":null,"abstract":"The controllers developed so far for the on-grid and off-grid operation is based on frequency regulation at grid and have yield poor switching by inducing oscillation. Hence to solve this problem in this paper, the switching between the on-grid and off-grid are made by the Emperor penguin based Adaptive fuzzy neuro inference system (EP-ANFIS) controller, which works based on the energy supplied to the load. To serve the transient load condition the hybrid storage unit is modelled by social-ski driver algorithm (SSD) that will schedule the energy supplement between the grid and generator. The proposed controller model provides stable operation during the switching process that is without the transient oscillation the switching is smooth. The error in data selection for the ANFIS is reduced by the Emperor penguin optimization (EPO) as 0.1 for the test data. The proposed work is implemented in Matlab/Simulink platform. The results are compared with the existing works in terms of voltage, current, power, and THD. When examining for transient load condition the settling time for the controller to the steady state is 0.78 s which is comparatively low with PID, PI, ANFIS, and ANFIS-PSO controllers. The THD is reduced to 9.11% from the existing methods by maintain the fundamental frequency of 50 Hz.","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"99 1","pages":"336-349"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Design of Efficient Storage Unit and EP-ANFIS Controller for On-grid and Off-grid Connected PV-WT System\",\"authors\":\"S. V. Karemore, Eda Vijay Kumar\",\"doi\":\"10.3311/ppee.20364\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The controllers developed so far for the on-grid and off-grid operation is based on frequency regulation at grid and have yield poor switching by inducing oscillation. Hence to solve this problem in this paper, the switching between the on-grid and off-grid are made by the Emperor penguin based Adaptive fuzzy neuro inference system (EP-ANFIS) controller, which works based on the energy supplied to the load. To serve the transient load condition the hybrid storage unit is modelled by social-ski driver algorithm (SSD) that will schedule the energy supplement between the grid and generator. The proposed controller model provides stable operation during the switching process that is without the transient oscillation the switching is smooth. The error in data selection for the ANFIS is reduced by the Emperor penguin optimization (EPO) as 0.1 for the test data. The proposed work is implemented in Matlab/Simulink platform. The results are compared with the existing works in terms of voltage, current, power, and THD. When examining for transient load condition the settling time for the controller to the steady state is 0.78 s which is comparatively low with PID, PI, ANFIS, and ANFIS-PSO controllers. The THD is reduced to 9.11% from the existing methods by maintain the fundamental frequency of 50 Hz.\",\"PeriodicalId\":37664,\"journal\":{\"name\":\"Periodica polytechnica Electrical engineering and computer science\",\"volume\":\"99 1\",\"pages\":\"336-349\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Periodica polytechnica Electrical engineering and computer science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3311/ppee.20364\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Periodica polytechnica Electrical engineering and computer science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3311/ppee.20364","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
Design of Efficient Storage Unit and EP-ANFIS Controller for On-grid and Off-grid Connected PV-WT System
The controllers developed so far for the on-grid and off-grid operation is based on frequency regulation at grid and have yield poor switching by inducing oscillation. Hence to solve this problem in this paper, the switching between the on-grid and off-grid are made by the Emperor penguin based Adaptive fuzzy neuro inference system (EP-ANFIS) controller, which works based on the energy supplied to the load. To serve the transient load condition the hybrid storage unit is modelled by social-ski driver algorithm (SSD) that will schedule the energy supplement between the grid and generator. The proposed controller model provides stable operation during the switching process that is without the transient oscillation the switching is smooth. The error in data selection for the ANFIS is reduced by the Emperor penguin optimization (EPO) as 0.1 for the test data. The proposed work is implemented in Matlab/Simulink platform. The results are compared with the existing works in terms of voltage, current, power, and THD. When examining for transient load condition the settling time for the controller to the steady state is 0.78 s which is comparatively low with PID, PI, ANFIS, and ANFIS-PSO controllers. The THD is reduced to 9.11% from the existing methods by maintain the fundamental frequency of 50 Hz.
期刊介绍:
The main scope of the journal is to publish original research articles in the wide field of electrical engineering and informatics fitting into one of the following five Sections of the Journal: (i) Communication systems, networks and technology, (ii) Computer science and information theory, (iii) Control, signal processing and signal analysis, medical applications, (iv) Components, Microelectronics and Material Sciences, (v) Power engineering and mechatronics, (vi) Mobile Software, Internet of Things and Wearable Devices, (vii) Solid-state lighting and (viii) Vehicular Technology (land, airborne, and maritime mobile services; automotive, radar systems; antennas and radio wave propagation).