NatureInspired MPPT Algorithms for Partially Shaded PV Systems: A Comparative Study
Abstract
:1. Introduction
2. PV Modeling and Its Characteristic Curves
3. Intelligent Nature Inspired Algorithms: An Overview
 particle swarm optimization (PSO) algorithm
 differential evolution (DE) algorithm
 ant colony optimization (ACO) algorithm
 artificial bee colony (ABC) optimization algorithm
 bacteria foraging optimization algorithm (BFOA).
3.1. Particle Swarm Optimization (PSO)
3.2. Differential Evolution (DE)
3.2.1. Initialization
3.2.2. Mutation
3.2.3. Crossover
3.2.4. Selection
3.3. Ant Colony Optimization (ACO)
3.4. Artificial Bee Colony (ABC)
ABC as the MPPT
3.5. Bacteria Foraging Optimization Algorithm (BFOA)
3.5.1. Chemotaxis
3.5.2. Swarming
3.5.3. Reproduction
3.5.4. Elimination and Dispersal
4. Critical Evaluation of MPPT Algorithms
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Sl. No  Algorithm Name  Advantage  Disadvantage 

1  PSO 


2  ACO 


3  ABC 


4  DE 


5  BFOA 


Ref. No  Method Used  Year of Publication  System Under Consideration  Observed Condition  Converter Used  Advantages 

[25]  PSO combined with P&O  2015  Stand alone  PSC  dc–dc 

[31]  Dormant PSO and INC  2015  Stand alone  PSC  dc–dc 

[35]  PSO  2015  GridTied  PSC  dc–dc 

[32]  Enhanced Leader PSO (ELPSO)  2017  Stand alone  PSC  dc–dc 

[82]  PSO and INC  2017  Rooftop PV  PSC  dc–dc 

[26]  Combination of HL and SAPSO (HSAPSO)  2018  Stand alone  PSC  dc–dc 

[38]  DE  2010  Stand alone  PSC  dc–dc boost converter 

[47]  DE  2012  Stand alone  PSC  dc–dc boost converter 

[37]  DE with modified mutation direction  2014  Stand alone  PSC  dc–dc 

[48]  Improved DE  2018  Stand alone  PSC with varying load condition  dc–dc sepic converter 

[59]  Improved ACO based P&O  2016  Stand alone  PSC  dc–dc 

[50]  ACO  2016  Stand alone  PSC  dc–dc 

[56]  ACO  2013  Stand alone  PSC  dc–dc 

[57]  ACONew Pheromone Update Strategy (ACONPU)  2017  Stand alone  PSC  dc–dc 

[69]  ABC  2015  Stand alone  PSC  dc–dc 

[70]  ABC  2015  Stand alone  PSC  dc–dc 

[72]  MABC  2015  Stand alone  PSC  dc–dc 

[78]  BFOA tuned PI  2016  GridTied  Varying load conditions   

[80]  BPSO Fuzzy P&O  2017  Stand alone    dc–dc 

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Pathy, S.; Subramani, C.; Sridhar, R.; Thamizh Thentral, T.M.; Padmanaban, S. NatureInspired MPPT Algorithms for Partially Shaded PV Systems: A Comparative Study. Energies 2019, 12, 1451. https://doi.org/10.3390/en12081451
Pathy S, Subramani C, Sridhar R, Thamizh Thentral TM, Padmanaban S. NatureInspired MPPT Algorithms for Partially Shaded PV Systems: A Comparative Study. Energies. 2019; 12(8):1451. https://doi.org/10.3390/en12081451
Chicago/Turabian StylePathy, Somashree, C. Subramani, R. Sridhar, T. M. Thamizh Thentral, and Sanjeevikumar Padmanaban. 2019. "NatureInspired MPPT Algorithms for Partially Shaded PV Systems: A Comparative Study" Energies 12, no. 8: 1451. https://doi.org/10.3390/en12081451