MPPT in PV systems using ant colony optimisation with dwindling population

Ant colony optimisation has been tailored to suit maximum power point tracking (MPPT) in photovoltaic (PV) systems and is presented in this study. Artificial ants are deployed in the solution space and are made to forage and the ants which find better sources of food are retained while ants fail to search effectively are deleted from the population. The greedy search of potential ants for better food location leads to identification of higher power peaks in the PV system. The concept is modelled suitably and MPPT curves in a few PV configurations are simulated and found to be promising. Experiments were also conducted to show the veracity of the new method.

[1]  Sishaj P. Simon,et al.  Development of an Improved P&O Algorithm Assisted Through a Colony of Foraging Ants for MPPT in PV System , 2016, IEEE Transactions on Industrial Informatics.

[2]  K.W.E. Cheng,et al.  An improved ant colony optimization algorithm and its application to electromagnetic devices designs , 2005, IEEE Transactions on Magnetics.

[3]  Anula Khare,et al.  Sizing and performance analysis of standalone wind-photovoltaic based hybrid energy system using ant colony optimisation , 2016 .

[4]  Mazen Abdel-Salam,et al.  Adaptive reference voltage-based MPPT technique for PV applications , 2017 .

[5]  Yi-Hwa Liu,et al.  A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions , 2012, IEEE Transactions on Energy Conversion.

[6]  M. Seyedmahmoudian,et al.  Simulation and Hardware Implementation of New Maximum Power Point Tracking Technique for Partially Shaded PV System Using Hybrid DEPSO Method , 2015, IEEE Transactions on Sustainable Energy.

[7]  P.-C. Hsu,et al.  Analytical modelling of partial shading and different orientation of photovoltaic modules , 2010 .

[8]  Vivek Agarwal,et al.  Global maximum power point tracking of PV arrays under partial shading conditions using a modified particle velocity-based PSO technique , 2017 .

[9]  Stephen J. Finney,et al.  A Maximum Power Point Tracking Technique for Partially Shaded Photovoltaic Systems in Microgrids , 2013, IEEE Transactions on Industrial Electronics.

[10]  Bhim Singh,et al.  Maximum power point tracking technique using artificial bee colony and hill climbing algorithms during mismatch insolation conditions on PV array , 2018, IET Renewable Power Generation.

[11]  Osama A. Mohammed,et al.  Design and Hardware Implementation of FL-MPPT Control of PV Systems Based on GA and Small-Signal Analysis , 2017, IEEE Transactions on Sustainable Energy.

[12]  Marcello Chiaberge,et al.  A New Sensorless Hybrid MPPT Algorithm Based on Fractional Short-Circuit Current Measurement and P&O MPPT , 2015, IEEE Transactions on Sustainable Energy.

[13]  Mohammed Saeed,et al.  Modified particle swarm optimisation technique for optimal design of small renewable energy system supplying a specific load at Mansoura University , 2015 .

[14]  Barry W. Williams,et al.  Improved performance low-cost incremental conductance PV MPPT technique , 2016 .

[15]  P.L. Chapman,et al.  Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques , 2007, IEEE Transactions on Energy Conversion.

[16]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[17]  D. Petreus,et al.  A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading , 2014 .

[18]  Kinattingal Sundareswaran,et al.  MPPT of PV Systems Under Partial Shaded Conditions Through a Colony of Flashing Fireflies , 2014, IEEE Transactions on Energy Conversion.

[19]  Douglas L. Maskell,et al.  A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions , 2013 .

[20]  B. Zahawi,et al.  Assessment of Perturb and Observe MPPT Algorithm Implementation Techniques for PV Pumping Applications , 2012, IEEE Transactions on Sustainable Energy.

[21]  Sishaj P. Simon,et al.  Enhanced Energy Output From a PV System Under Partial Shaded Conditions Through Artificial Bee Colony , 2015, IEEE Transactions on Sustainable Energy.

[22]  Padmanathan Kasinathan,et al.  Crowded plant height optimisation algorithm tuned maximum power point tracking for grid integrated solar power conditioning system , 2019 .

[23]  Bidyadhar Subudhi,et al.  A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power Systems , 2013, IEEE Transactions on Sustainable Energy.

[24]  Vivek Agarwal,et al.  Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions , 2008, IEEE Transactions on Industrial Electronics.

[25]  S.L. Ho,et al.  A modified ant colony optimization algorithm modeled on tabu-search methods , 2006, IEEE Transactions on Magnetics.

[26]  Himavathi Srinivasan,et al.  Simplified accelerated particle swarm optimisation algorithm for efficient maximum power point tracking in partially shaded photovoltaic systems , 2016 .