Hardware implementation of a novel hybrid MPPT technique for fast tracking of GMPP in solar PV system under PSCs

Purpose To generate electricity, solar photovoltaic (PV) systems are among the best, most eco-friendly and most cost-effective solutions available. Extraction of maximum possible electricity from the solar PV system is complicated by a number of factors brought on by the ever-changing weather conditions under which it must operate. Many conventional and evolutionary algorithm-based maximum power point tracking (MPPT) techniques have the limitation of not being able to extract maximum power under partial shade and rapidly varying irradiance. Hence, the purpose of this paper is to propose a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid bridge link-honey comb (BL-HC) configured PV system to enhance the better maximum power during dynamic and steady state operations within less time. Design/methodology/approach In this method, a hybridization of two algorithms is proposed to track the true with faster convergence under PSCs. Initially, the slime mould optimization (SMO) algorithm is initiated for exploration of optimum duty cycles and later P&O algorithm is initiated for exploitation of global duty cycle for the DC–DC converter to operate at GMPP and for fast convergence. Findings The effectiveness of the proposed HSMO MPPT is compared with adaptive coefficient particle swarm optimization (ACPSO), flower pollination algorithm and SMO MPPT techniques in terms of tracked GMPP, convergence time/tracking speed and efficacy under six complex partial shading conditions. From the results, it is noticed that the proposed algorithm tracks the true GMPP under most of the shading conditions with less tracking time when compared to other MPPT techniques. Originality/value This paper proposes a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid BL-HC configured PV system enhance the better maximum power under partial shading conditions (PSCs). This method operated in two stages as SMO for exploration and P&O for exploitation for faster convergence and to track true GMPP under PSCs. The proposed approach largely improves the performance of the MPP tracking of the PV systems. Initially, the proposed MPPT technique is simulated in MATLAB/Simulink environment. Furthermore, an experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution.

[1]  Vikram Kumar Kamboj,et al.  Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical and engineering design problems , 2022, Complex & Intelligent Systems.

[2]  M. Y. Javed,et al.  A Novel Hybrid MPPT Technique Based on Harris Hawk Optimization (HHO) and Perturb and Observer (P&O) under Partial and Complex Partial Shading Conditions , 2022, Energies.

[3]  Krzysztof Ejsmont,et al.  A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions , 2022, Energies.

[4]  Praveen Kumar Bonthagorla,et al.  Application of Radial Basis Neural Network in MPPT Technique for Stand-Alone PV System Under Partial Shading Conditions , 2021, IETE Journal of Research.

[5]  Niraj Kumar Dewangan,et al.  A Flying Squirrel Search Optimization for MPPT Under Partial Shaded Photovoltaic System , 2021, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[6]  Saad Mekhilef,et al.  A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions , 2021 .

[7]  Qiang Ling,et al.  A novel MPPT technique based on Henry gas solubility optimization , 2020 .

[8]  Noureddine Bouarroudj,et al.  An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using Crow Search Algorithm (CSA) , 2020 .

[9]  Loi Lei Lai,et al.  A dynamic particles MPPT method for photovoltaic systems under partial shading conditions , 2020 .

[10]  N. Rajasekar,et al.  An Accurate, Shade Detection-Based Hybrid Maximum Power Point Tracking Approach for PV Systems , 2020, IEEE Transactions on Power Electronics.

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

[12]  Suresh Mikkili,et al.  PV Distributed-MPP Tracking: Total-Cross-Tied Configuration of String-Integrated-Converters to Extract the Maximum Power Under Various PSCs , 2020, IEEE Systems Journal.

[13]  Suresh Mikkili,et al.  Critical Review on PV MPPT Techniques: Classical, Intelligent and Optimisation , 2020, IET Renewable Power Generation.

[14]  Performance investigation of hybrid and conventional PV array configurations for grid-connected/standalone PV systems , 2020, CSEE Journal of Power and Energy Systems.

[15]  Ali N. Hasan,et al.  A Comprehensive Review on a PV Based System to Harvest Maximum Power , 2019, Electronics.

[16]  Qiang Ling,et al.  Novel MPPT techniques for photovoltaic systems under uniform irradiance and Partial shading , 2019, Solar Energy.

[17]  Ali M. Eltamaly,et al.  Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC , 2019, Solar Energy.

[18]  Ch Hussaian Basha,et al.  Development of Cuckoo Search MPPT Algorithm for Partially Shaded Solar PV SEPIC Converter , 2019, SocProS.

[19]  Tingting Pei,et al.  A Novel Global Maximum Power Point Tracking Strategy Based on Modified Flower Pollination Algorithm for Photovoltaic Systems under Non-Uniform Irradiation and Temperature Conditions , 2018, Energies.

[20]  Shahrin Md. Ayob,et al.  Differential Evolution Based Solar Photovoltaic Array Reconfiguration Algorithm for Optimal Energy Extraction during Partial Shading Condition , 2018, International Journal of Power Electronics and Drive Systems (IJPEDS).

[21]  V. Kamala Devi,et al.  A modified Perturb & Observe MPPT technique to tackle steady state and rapidly varying atmospheric conditions , 2017 .

[22]  Pratap Nair,et al.  A Review on Photo Voltaic MPPT Algorithms , 2016 .

[23]  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.

[24]  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.

[25]  Saad Mekhilef,et al.  Implementation of a modified incremental conductance MPPT algorithm with direct control based on a fuzzy duty cycle change estimator using dSPACE , 2014 .

[26]  Subhadeep Bhattacharjee,et al.  A comparative study on converter topologies for maximum power point tracking application in photovoltaic generation , 2014 .

[27]  Parimita Mohanty,et al.  MATLAB based modeling to study the performance of different MPPT techniques used for solar PV system under various operating conditions , 2014 .

[28]  Chee Wei Tan,et al.  A comprehensive review of maximum power point tracking algorithms for photovoltaic systems , 2014 .

[29]  Anula Khare,et al.  A review of particle swarm optimization and its applications in Solar Photovoltaic system , 2013, Appl. Soft Comput..

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

[31]  T. Ueda,et al.  Interaction between cell shape and contraction pattern in the Physarum plasmodium. , 2000, Biophysical chemistry.