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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Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 13; s. 5973
Hlavní autoři: Sahoo, Gagan Kumar, Choudhury, Subhashree, Rathore, Rajkumar Singh, Bajaj, Mohit
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 27.06.2023
MDPI
Témata:
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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Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23135973