Enhancement of Extracted Power from Photovoltaic Systems Through Accelerated Particle Swarm Optimisation Based MPPT

This paper presents a maximum power point tracking (MPPT) technique for photovoltaic (PV) system based on accelerated particle swarm optimisation (APSO) algorithm. The main purpose is to handle the multi-modal P-V characteristic curve under partial shading conditions. The APSO based MPPT has the advantages to be simple and accurate to track the global maximum power point (GMPP) under severe partial shading patterns. The simulation results have shown that the proposed technique can find the GMPP with high speed and efficiency and presents good dynamic behaviour. In addition, this technique is superior than the conventional Perturb and Observe (P&O) based MPPT in global peak tracking.

[1]  Simon Fong,et al.  Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications , 2011, NDT.

[2]  Douglas L. Maskell,et al.  Computational intelligence techniques for maximum power point tracking in PV systems: A review , 2018 .

[3]  Karim Kaced,et al.  FPGA implementation of PSO based MPPT for PV systems under partial shading conditions , 2017, 2017 6th International Conference on Systems and Control (ICSC).

[4]  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..

[5]  Yu‐Sheng Lin,et al.  Improved particle swarm optimization for maximum power point tracking in photovoltaic module arrays , 2015 .

[6]  C. Larbes,et al.  Bat algorithm based maximum power point tracking for photovoltaic system under partial shading conditions , 2017 .

[7]  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.

[8]  Masafumi Miyatake,et al.  Maximum Power Point Tracking of Multiple Photovoltaic Arrays: A PSO Approach , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Shahrin Md. Ayob,et al.  Evolutionary based maximum power point tracking technique using differential evolution algorithm , 2013 .

[10]  Kashif Ishaque,et al.  A Deterministic Particle Swarm Optimization Maximum Power Point Tracker for Photovoltaic System Under Partial Shading Condition , 2013, IEEE Transactions on Industrial Electronics.

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

[12]  Matthew Orosz,et al.  Evolution and feasibility of decentralized concentrating solar thermal power systems for modern energy access in rural areas , 2016 .