A Novel Prairie Dog-Based Meta-Heuristic Optimization Algorithm for Improved Control, Better Transient Response, and Power Quality Enhancement of Hybrid Microgrids

The growing demand for electricity driven by population growth and industrialization is met by integrating hybrid renewable energy sources (HRESs) into the grid. HRES integration improves reliability, reduces losses, and addresses power quality issues for safe and effective microgrid (MG) operation,...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 23; číslo 13; s. 5973
Hlavní autori:	Sahoo, Gagan Kumar, Choudhury, Subhashree, Rathore, Rajkumar Singh, Bajaj, Mohit
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 27.06.2023 MDPI
Predmet:	Algorithms Alternative energy sources Analysis Batteries battery Efficiency Energy resources Energy storage Fossil fuels fuel cell Fuel cell industry Fuel cells hybrid renewable energy sources Mathematical optimization Neural networks Optimization algorithms Optimization techniques photovoltaics power quality Prairie dogs proportional integral (PI) Renewable resources bee colony optimization fuel cell power quality thermal exchange optimization battery hybrid renewable energy sources photovoltaics proportional integral (PI)
ISSN:	1424-8220, 1424-8220
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Abstract	The growing demand for electricity driven by population growth and industrialization is met by integrating hybrid renewable energy sources (HRESs) into the grid. HRES integration improves reliability, reduces losses, and addresses power quality issues for safe and effective microgrid (MG) operation, requiring efficient controllers. In this regard, this article proposes a prairie dog optimization (PDO) algorithm for the photovoltaic (PV)-, fuel cell (FC)-, and battery-based HRESs designed in MATLAB/Simulink architecture. The proposed PDO method optimally tunes the proportional integral (PI) controller gain parameters to achieve effective compensation of load demand and mitigation of PQ problems. The MG system has been applied to various intentional PQ issues such as swell, unbalanced load, oscillatory transient, and notch conditions to study the response of the proposed PDO controller. For evaluating the efficacy of the proposed PDO algorithm, the simulation results obtained are compared with those of earlier popular methodologies utilized in the current literature such as bee colony optimization (BCO), thermal exchange optimization, and PI techniques. A detailed analysis of the results found emphasizes the efficiency, robustness, and potential of the suggested PDO controller in significantly improving the overall system operation by minimizing the THD, improving the control of active and reactive power, enhancing the power factor, lowering the voltage deviation, and keeping the terminal voltage, DC-link voltage, grid voltage, and grid current almost constant in the event of PQ fault occurrence. As a result, the proposed PDO method paves the way for real-time employment in the MG system.
AbstractList	The growing demand for electricity driven by population growth and industrialization is met by integrating hybrid renewable energy sources (HRESs) into the grid. HRES integration improves reliability, reduces losses, and addresses power quality issues for safe and effective microgrid (MG) operation, requiring efficient controllers. In this regard, this article proposes a prairie dog optimization (PDO) algorithm for the photovoltaic (PV)-, fuel cell (FC)-, and battery-based HRESs designed in MATLAB/Simulink architecture. The proposed PDO method optimally tunes the proportional integral (PI) controller gain parameters to achieve effective compensation of load demand and mitigation of PQ problems. The MG system has been applied to various intentional PQ issues such as swell, unbalanced load, oscillatory transient, and notch conditions to study the response of the proposed PDO controller. For evaluating the efficacy of the proposed PDO algorithm, the simulation results obtained are compared with those of earlier popular methodologies utilized in the current literature such as bee colony optimization (BCO), thermal exchange optimization, and PI techniques. A detailed analysis of the results found emphasizes the efficiency, robustness, and potential of the suggested PDO controller in significantly improving the overall system operation by minimizing the THD, improving the control of active and reactive power, enhancing the power factor, lowering the voltage deviation, and keeping the terminal voltage, DC-link voltage, grid voltage, and grid current almost constant in the event of PQ fault occurrence. As a result, the proposed PDO method paves the way for real-time employment in the MG system. The growing demand for electricity driven by population growth and industrialization is met by integrating hybrid renewable energy sources (HRESs) into the grid. HRES integration improves reliability, reduces losses, and addresses power quality issues for safe and effective microgrid (MG) operation, requiring efficient controllers. In this regard, this article proposes a prairie dog optimization (PDO) algorithm for the photovoltaic (PV)-, fuel cell (FC)-, and battery-based HRESs designed in MATLAB/Simulink architecture. The proposed PDO method optimally tunes the proportional integral (PI) controller gain parameters to achieve effective compensation of load demand and mitigation of PQ problems. The MG system has been applied to various intentional PQ issues such as swell, unbalanced load, oscillatory transient, and notch conditions to study the response of the proposed PDO controller. For evaluating the efficacy of the proposed PDO algorithm, the simulation results obtained are compared with those of earlier popular methodologies utilized in the current literature such as bee colony optimization (BCO), thermal exchange optimization, and PI techniques. A detailed analysis of the results found emphasizes the efficiency, robustness, and potential of the suggested PDO controller in significantly improving the overall system operation by minimizing the THD, improving the control of active and reactive power, enhancing the power factor, lowering the voltage deviation, and keeping the terminal voltage, DC-link voltage, grid voltage, and grid current almost constant in the event of PQ fault occurrence. As a result, the proposed PDO method paves the way for real-time employment in the MG system.The growing demand for electricity driven by population growth and industrialization is met by integrating hybrid renewable energy sources (HRESs) into the grid. HRES integration improves reliability, reduces losses, and addresses power quality issues for safe and effective microgrid (MG) operation, requiring efficient controllers. In this regard, this article proposes a prairie dog optimization (PDO) algorithm for the photovoltaic (PV)-, fuel cell (FC)-, and battery-based HRESs designed in MATLAB/Simulink architecture. The proposed PDO method optimally tunes the proportional integral (PI) controller gain parameters to achieve effective compensation of load demand and mitigation of PQ problems. The MG system has been applied to various intentional PQ issues such as swell, unbalanced load, oscillatory transient, and notch conditions to study the response of the proposed PDO controller. For evaluating the efficacy of the proposed PDO algorithm, the simulation results obtained are compared with those of earlier popular methodologies utilized in the current literature such as bee colony optimization (BCO), thermal exchange optimization, and PI techniques. A detailed analysis of the results found emphasizes the efficiency, robustness, and potential of the suggested PDO controller in significantly improving the overall system operation by minimizing the THD, improving the control of active and reactive power, enhancing the power factor, lowering the voltage deviation, and keeping the terminal voltage, DC-link voltage, grid voltage, and grid current almost constant in the event of PQ fault occurrence. As a result, the proposed PDO method paves the way for real-time employment in the MG system.
Audience	Academic
Author	Rathore, Rajkumar Singh Choudhury, Subhashree Sahoo, Gagan Kumar Bajaj, Mohit
AuthorAffiliation	3 Cardiff School of Technologies, Cardiff Metropolitan University, Llandaff Campus, Western Avenue, Cardiff CF5 2YB, UK 2 Department of EEE, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India 5 Department of Electrical Engineering, Graphic Era Hill University, Dehradun 248002, India 1 Department of EE, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India 4 Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India 6 Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan
AuthorAffiliation_xml	– name: 6 Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan – name: 1 Department of EE, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India – name: 3 Cardiff School of Technologies, Cardiff Metropolitan University, Llandaff Campus, Western Avenue, Cardiff CF5 2YB, UK – name: 2 Department of EEE, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India – name: 5 Department of Electrical Engineering, Graphic Era Hill University, Dehradun 248002, India – name: 4 Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India
Author_xml	– sequence: 1 givenname: Gagan Kumar surname: Sahoo fullname: Sahoo, Gagan Kumar – sequence: 2 givenname: Subhashree orcidid: 0000-0003-1872-7147 surname: Choudhury fullname: Choudhury, Subhashree – sequence: 3 givenname: Rajkumar Singh orcidid: 0000-0003-4571-1888 surname: Rathore fullname: Rathore, Rajkumar Singh – sequence: 4 givenname: Mohit orcidid: 0000-0002-1086-457X surname: Bajaj fullname: Bajaj, Mohit
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Energy Storage doi: 10.1016/j.est.2022.103966
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SubjectTerms	Algorithms Alternative energy sources Analysis Batteries battery Efficiency Energy resources Energy storage Fossil fuels fuel cell Fuel cell industry Fuel cells hybrid renewable energy sources Mathematical optimization Neural networks Optimization algorithms Optimization techniques photovoltaics power quality Prairie dogs proportional integral (PI) Renewable resources
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Title	A Novel Prairie Dog-Based Meta-Heuristic Optimization Algorithm for Improved Control, Better Transient Response, and Power Quality Enhancement of Hybrid Microgrids
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