Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications

Purpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result...

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Main Authors: Ch. Sathish, I. A. Chidambaram, M. Manikandan
Format: Article
Language:English
Published: National Technical University "Kharkiv Polytechnic Institute" 2023-01-01
Series:Electrical engineering & Electromechanics
Subjects:
Online Access:http://eie.khpi.edu.ua/article/view/258349/266673
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author Ch. Sathish
I. A. Chidambaram
M. Manikandan
author_facet Ch. Sathish
I. A. Chidambaram
M. Manikandan
author_sort Ch. Sathish
collection DOAJ
description Purpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result of rising demand for electricity, rapid fossil fuel exhaustion and the threat of global warming. However, due to their inherent intermittency, these sources offer low system reliability. So, a hybrid energy system that encompasses wind/photovoltaic/battery is implemented in order to obtain a stable and reliable microgrid. Both solar and wind energy is easily accessible with huge untapped potential and together they account for more than 60 % of yearly net new electricity generation capacity additions around the world. Novelty. A KY converter is adopted here for enhancing the output of the photovoltaic system and its operation is controlled with the help of a cascaded an adaptive neuro fuzzy interface system controller. Originality. Increase of the overall system stability and reliability using hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Practical value. A proportional integral controller is used in the doubly fed induction generator based wind energy conversion system for controlling the operation of the pulse width modulation rectifier in order to deliver a controlled DC output voltage. A battery energy storage system, which uses a battery converter to be connected to the DC link, stores the excess power generated from the renewable energy sources. Based on the battery’s state of charge, its charging and discharging operation is controlled using a proportional integral controller. The controlled DC link voltage is fed to the three phase voltage source inverter for effective DC to AC voltage conversion. The inverter is connected to the three phase grid via an LC filter for effective harmonics mitigation. A proportional integral controller is used for achieving effective grid voltage synchronization. Results. The proposed model is simulated using MATLAB/Simulink, and from the obtained outcomes, it is noted that the cascaded adaptive neuro fuzzy interface system controller assisted KY converter is capable of maintaining the stable operation of the microgrid with an excellent efficiency of 93 %.
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spelling doaj.art-c8e486e9266745e8917bda353b9dc3132023-01-06T10:44:06ZengNational Technical University "Kharkiv Polytechnic Institute"Electrical engineering & Electromechanics2074-272X2309-34042023-01-011637010.20998/2074-272X.2023.1.09Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applicationsCh. Sathish0https://orcid.org/0000-0003-2119-0395I. A. Chidambaram1https://orcid.org/0000-0003-1242-9210M. Manikandan2https://orcid.org/0000-0002-4881-8157Annamalai University, IndiaAnnamalai University, IndiaJyothishmathi Institute of Technology and Science, IndiaPurpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result of rising demand for electricity, rapid fossil fuel exhaustion and the threat of global warming. However, due to their inherent intermittency, these sources offer low system reliability. So, a hybrid energy system that encompasses wind/photovoltaic/battery is implemented in order to obtain a stable and reliable microgrid. Both solar and wind energy is easily accessible with huge untapped potential and together they account for more than 60 % of yearly net new electricity generation capacity additions around the world. Novelty. A KY converter is adopted here for enhancing the output of the photovoltaic system and its operation is controlled with the help of a cascaded an adaptive neuro fuzzy interface system controller. Originality. Increase of the overall system stability and reliability using hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Practical value. A proportional integral controller is used in the doubly fed induction generator based wind energy conversion system for controlling the operation of the pulse width modulation rectifier in order to deliver a controlled DC output voltage. A battery energy storage system, which uses a battery converter to be connected to the DC link, stores the excess power generated from the renewable energy sources. Based on the battery’s state of charge, its charging and discharging operation is controlled using a proportional integral controller. The controlled DC link voltage is fed to the three phase voltage source inverter for effective DC to AC voltage conversion. The inverter is connected to the three phase grid via an LC filter for effective harmonics mitigation. A proportional integral controller is used for achieving effective grid voltage synchronization. Results. The proposed model is simulated using MATLAB/Simulink, and from the obtained outcomes, it is noted that the cascaded adaptive neuro fuzzy interface system controller assisted KY converter is capable of maintaining the stable operation of the microgrid with an excellent efficiency of 93 %. http://eie.khpi.edu.ua/article/view/258349/266673photovoltaic systemhybrid energy systemproportional integral controlleradaptive neuro fuzzy interface system controller
spellingShingle Ch. Sathish
I. A. Chidambaram
M. Manikandan
Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications
Electrical engineering & Electromechanics
photovoltaic system
hybrid energy system
proportional integral controller
adaptive neuro fuzzy interface system controller
title Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications
title_full Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications
title_fullStr Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications
title_full_unstemmed Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications
title_short Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications
title_sort intelligent cascaded adaptive neuro fuzzy interface system controller fed ky converter for hybrid energy based microgrid applications
topic photovoltaic system
hybrid energy system
proportional integral controller
adaptive neuro fuzzy interface system controller
url http://eie.khpi.edu.ua/article/view/258349/266673
work_keys_str_mv AT chsathish intelligentcascadedadaptiveneurofuzzyinterfacesystemcontrollerfedkyconverterforhybridenergybasedmicrogridapplications
AT iachidambaram intelligentcascadedadaptiveneurofuzzyinterfacesystemcontrollerfedkyconverterforhybridenergybasedmicrogridapplications
AT mmanikandan intelligentcascadedadaptiveneurofuzzyinterfacesystemcontrollerfedkyconverterforhybridenergybasedmicrogridapplications