A New Immersion and Invariance Control and Stable Deep Learning Fuzzy Approach for Power/Voltage Control Problem
Background: The use of renewable energies is extended due to their valuable features such as abundant and clarity. The microgrids that include the renewable energies are widely used in various applications such as power supplying of remote areas, increasing the network reliability, reducing the gree...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9658536/ |
| _version_ | 1811309353767010304 |
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| author | Mohammad Hosein Sabzalian Khalid A. Alattas Mauricio Aredes Abdullah K. Alanazi Hala M. Abo-Dief Ardashir Mohammadzadeh Saleh Mobayen Bruno Wanderley Franca Afef Fekih |
| author_facet | Mohammad Hosein Sabzalian Khalid A. Alattas Mauricio Aredes Abdullah K. Alanazi Hala M. Abo-Dief Ardashir Mohammadzadeh Saleh Mobayen Bruno Wanderley Franca Afef Fekih |
| author_sort | Mohammad Hosein Sabzalian |
| collection | DOAJ |
| description | Background: The use of renewable energies is extended due to their valuable features such as abundant and clarity. The microgrids that include the renewable energies are widely used in various applications such as power supplying of remote areas, increasing the network reliability, reducing the greenhouse gas emission, reducing the consumption demand, eliminating the consumption peaks, and so on. But, energy management in the these systems in an challenging problem. Because, there are some natural perturbations such as variation output load, grid-side faults and changes of irradiation and temperature. Aim and Objective: The problem is to design a controller to regulate the output voltage/energy under aforementioned disturbances. Methods: The paper presents a new approach for energy management in Photovoltaic (PV)/Battery/Fuel Cells (FC) systems. The uncertainties are compensated by the new optimization rules based on Immersion and Invariance (I&I) theorem and proposed deep learning type-2 fuzzy logic compensator (T2FLC). The objective function of T2FLC is to minimize the tracking error in presence of perturbations. The adaptation rules are derived such that the I&I stabilization criterions are satisfied. Both rules and fuzzy sets (FSs) of T2FLCs are optimized by guaranteed stability rules to tackle the effect of perturbations and estimation errors. Results and Discussion: It is shown that a well voltage/energy regulation performance is achieved under variation of temperature, suddenly changes of load and variation of irradiation. A comparison with similar controllers demonstrates the superiority of the suggested approach. Conclusion: The suggested regulator do not depend on the mathematical models, and results in good accuracy under difficult conditions, then it can be used in various applications. |
| first_indexed | 2024-04-13T09:40:45Z |
| format | Article |
| id | doaj.art-31d4a72aaa7b420580452add5a2c2e4d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T09:40:45Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-31d4a72aaa7b420580452add5a2c2e4d2022-12-22T02:51:56ZengIEEEIEEE Access2169-35362022-01-0110688110.1109/ACCESS.2021.31377199658536A New Immersion and Invariance Control and Stable Deep Learning Fuzzy Approach for Power/Voltage Control ProblemMohammad Hosein Sabzalian0Khalid A. Alattas1https://orcid.org/0000-0001-6528-3636Mauricio Aredes2Abdullah K. Alanazi3Hala M. Abo-Dief4Ardashir Mohammadzadeh5https://orcid.org/0000-0003-4468-8604Saleh Mobayen6https://orcid.org/0000-0002-5676-1875Bruno Wanderley Franca7https://orcid.org/0000-0003-0788-7997Afef Fekih8https://orcid.org/0000-0003-4522-502XLaboratory of Power Electronics and Medium Voltage Applications (LEMT), Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, BrazilDepartment of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaLaboratory of Power Electronics and Medium Voltage Applications (LEMT), Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, BrazilDepartment of Chemistry, Collage of Science, Taif University, Taif, Saudi ArabiaDepartment of Chemistry, Collage of Science, Taif University, Taif, Saudi ArabiaInstitute of Research and Development, Duy Tan University, Da Nang, VietnamFuture Technology Research Center, National Yunlin University of Science and Technology, Douliu, TaiwanElectrical Engineering Department, Fluminense Federal University, Niterói, BrazilDepartment of Electrical and Computer Engineering, University of Louisiana at Lafayette, Lafayette, LA, USABackground: The use of renewable energies is extended due to their valuable features such as abundant and clarity. The microgrids that include the renewable energies are widely used in various applications such as power supplying of remote areas, increasing the network reliability, reducing the greenhouse gas emission, reducing the consumption demand, eliminating the consumption peaks, and so on. But, energy management in the these systems in an challenging problem. Because, there are some natural perturbations such as variation output load, grid-side faults and changes of irradiation and temperature. Aim and Objective: The problem is to design a controller to regulate the output voltage/energy under aforementioned disturbances. Methods: The paper presents a new approach for energy management in Photovoltaic (PV)/Battery/Fuel Cells (FC) systems. The uncertainties are compensated by the new optimization rules based on Immersion and Invariance (I&I) theorem and proposed deep learning type-2 fuzzy logic compensator (T2FLC). The objective function of T2FLC is to minimize the tracking error in presence of perturbations. The adaptation rules are derived such that the I&I stabilization criterions are satisfied. Both rules and fuzzy sets (FSs) of T2FLCs are optimized by guaranteed stability rules to tackle the effect of perturbations and estimation errors. Results and Discussion: It is shown that a well voltage/energy regulation performance is achieved under variation of temperature, suddenly changes of load and variation of irradiation. A comparison with similar controllers demonstrates the superiority of the suggested approach. Conclusion: The suggested regulator do not depend on the mathematical models, and results in good accuracy under difficult conditions, then it can be used in various applications.https://ieeexplore.ieee.org/document/9658536/Energy managementimmersion and invariancedeep learningfuzzy systemsvoltage controlstability |
| spellingShingle | Mohammad Hosein Sabzalian Khalid A. Alattas Mauricio Aredes Abdullah K. Alanazi Hala M. Abo-Dief Ardashir Mohammadzadeh Saleh Mobayen Bruno Wanderley Franca Afef Fekih A New Immersion and Invariance Control and Stable Deep Learning Fuzzy Approach for Power/Voltage Control Problem IEEE Access Energy management immersion and invariance deep learning fuzzy systems voltage control stability |
| title | A New Immersion and Invariance Control and Stable Deep Learning Fuzzy Approach for Power/Voltage Control Problem |
| title_full | A New Immersion and Invariance Control and Stable Deep Learning Fuzzy Approach for Power/Voltage Control Problem |
| title_fullStr | A New Immersion and Invariance Control and Stable Deep Learning Fuzzy Approach for Power/Voltage Control Problem |
| title_full_unstemmed | A New Immersion and Invariance Control and Stable Deep Learning Fuzzy Approach for Power/Voltage Control Problem |
| title_short | A New Immersion and Invariance Control and Stable Deep Learning Fuzzy Approach for Power/Voltage Control Problem |
| title_sort | new immersion and invariance control and stable deep learning fuzzy approach for power voltage control problem |
| topic | Energy management immersion and invariance deep learning fuzzy systems voltage control stability |
| url | https://ieeexplore.ieee.org/document/9658536/ |
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