A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions

Abstract The partial shading condition (PSC) makes it challenging for the PV system to harvest maximum power via maximum power point tracking (MPPT). Various MPPT algorithms based on bio-inspired optimization methods were proposed in the literature. The mechanism employed by these algorithms varies from one to another, making them perform differently when tracking the GMPP. This paper introduces a novel MPPT technique based on the improved squirrel search algorithm (ISSA). The performance of the proposed ISSA improved the tracking time by 50% in comparison with the conventional SSA algorithm. Similarly, the proposed method has also been compared with popular Genetic algorithm (GA), and particle swarm optimization (PSO). The results proved the ability of the proposed algorithm in tracking the GMPP with faster convergence and fewer power oscillations in comparison. The feasibility and effectiveness of the proposed ISSA based MPPT have been validated experimentally, and the results clearly demonstrate its capability in tracking the GMPP with an average efficiency of 99.48% and average tracking time of 0.66 s.

[1]  S. Mekhilef,et al.  Improved Differential Evolution-Based MPPT Algorithm Using SEPIC for PV Systems Under Partial Shading Conditions and Load Variation , 2018, IEEE Transactions on Industrial Informatics.

[2]  Mujahed Al-Dhaifallah,et al.  Design and Hardware Implementation of New Adaptive Fuzzy Logic-Based MPPT Control Method for Photovoltaic Applications , 2019, IEEE Access.

[3]  Kok Soon Tey,et al.  Implementation of BAT Algorithm as Maximum Power Point Tracking Technique for Photovoltaic System Under Partial Shading Conditions , 2018, 2018 IEEE Energy Conversion Congress and Exposition (ECCE).

[4]  Navid Mousavi,et al.  The Design and Construction of a High Efficiency Satellite Electrical Power Supply System , 2016 .

[5]  S. Saravanan,et al.  RBFN based MPPT algorithm for PV system with high step up converter , 2016 .

[6]  Zhen Zhang,et al.  Characteristic output of PV systems under partial shading or mismatch conditions , 2015 .

[7]  Mazen Abdel-Salam,et al.  Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications: A review and a novel hybrid approach , 2018, Energy Conversion and Management.

[8]  Baoqun Yin,et al.  A Salp-Swarm Optimization based MPPT technique for harvesting maximum energy from PV systems under partial shading conditions , 2020 .

[9]  Saad Mekhilef,et al.  Simulation and Hardware Implementation of Incremental Conductance MPPT With Direct Control Method Using Cuk Converter , 2011, IEEE Transactions on Industrial Electronics.

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

[11]  Saad Mekhilef,et al.  State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review , 2016 .

[12]  Stefan Daraban,et al.  A novel global MPPT based on genetic algorithms for photovoltaic systems under the influence of partial shading , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[13]  S. Mekhilef,et al.  Maximum Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions Using Bat Algorithm , 2018 .

[14]  Jie Ji,et al.  Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review , 2018 .

[15]  Bidyadhar Subudhi,et al.  A New MPPT Design Using Grey Wolf Optimization Technique for Photovoltaic System Under Partial Shading Conditions , 2016, IEEE Transactions on Sustainable Energy.

[16]  C. Larbes,et al.  A review of global maximum power point tracking techniques of photovoltaic system under partial shading conditions , 2018, Renewable and Sustainable Energy Reviews.

[17]  S. Kanthalakshmi,et al.  An improved P&O algorithm integrated with artificial bee colony for photovoltaic systems under partial shading conditions , 2019, Solar Energy.

[18]  Shailja Singh,et al.  Maximum power point tracking of solar photovoltaic system using modified perturbation and observation method , 2015 .

[19]  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.

[20]  Tao Yu,et al.  Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition , 2019, Journal of Cleaner Production.

[21]  Aissa Chouder,et al.  Study of bypass diodes configuration on PV modules , 2009 .

[22]  Aissa Chouder,et al.  Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions , 2015, Appl. Soft Comput..

[23]  Mazen Abdel-Salam,et al.  An improved perturb-and-observe based MPPT method for PV systems under varying irradiation levels , 2018, Solar Energy.

[24]  Hocine Labar,et al.  Real time partial shading detection and global maximum power point tracking applied to outdoor PV panel boost converter , 2018, Energy Conversion and Management.

[25]  Almoataz Y. Abdelaziz,et al.  A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions , 2017 .

[26]  Kok Soon Tey,et al.  Modified Incremental Conductance Algorithm for Photovoltaic System Under Partial Shading Conditions and Load Variation , 2014, IEEE Transactions on Industrial Electronics.

[27]  Edris Pouresmaeil,et al.  Integration of Large Scale PV-Based Generation into Power Systems: A Survey , 2019, Energies.

[28]  Ali M. Eltamaly,et al.  A novel evaluation index for the photovoltaic maximum power point tracker techniques , 2018, Solar Energy.

[29]  Kok Soon Tey,et al.  Advancement of voltage equalizer topologies for serially connected solar modules as partial shading mitigation technique: A comprehensive review , 2020 .

[30]  Jubaer Ahmed,et al.  A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability , 2014 .

[31]  Karima Benatchba,et al.  A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions , 2017, Appl. Soft Comput..

[32]  Xinghuo Yu,et al.  An Overall Distribution Particle Swarm Optimization MPPT Algorithm for Photovoltaic System Under Partial Shading , 2019, IEEE Transactions on Industrial Electronics.

[33]  Vijander Singh,et al.  A novel nature-inspired algorithm for optimization: Squirrel search algorithm , 2019, Swarm Evol. Comput..

[34]  Adeel Feroz Mirza,et al.  Novel Grass Hopper optimization based MPPT of PV systems for complex partial shading conditions , 2020 .

[35]  Muhammad Amjad,et al.  A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm , 2012 .

[36]  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.

[37]  Kashif Ishaque,et al.  An Improved Particle Swarm Optimization (PSO)–Based MPPT for PV With Reduced Steady-State Oscillation , 2012, IEEE Transactions on Power Electronics.

[38]  Okan Bingöl,et al.  Analysis and comparison of different PV array configurations under partial shading conditions , 2018 .

[39]  Tao Yu,et al.  Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition , 2019, Energy Conversion and Management.

[40]  Santi Agatino Rizzo,et al.  ANN based MPPT method for rapidly variable shading conditions , 2015 .