Open AccessGlobal maximum power point tracking of PV arrays under partial shading conditions using a modified particle velocity‐based PSO technique
Author(s) -
Sen Tanuj,
Pragallapati Nataraj,
Agarwal Vivek,
Kumar Rajneesh
Publication year - 2018
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2016.0838
Subject(s) - particle swarm optimization , tracking (education) , control theory (sociology) , acceleration , matlab , position (finance) , randomness , maxima and minima , power (physics) , maximum power point tracking , algorithm , mathematical optimization , point (geometry) , computer science , mathematics , physics , artificial intelligence , mathematical analysis , psychology , pedagogy , geometry , control (management) , quantum mechanics , inverter , statistics , finance , classical mechanics , economics , operating system
Modern PV arrays are generally designed with bypass diodes to avoid damage. However, such arrays exhibit multiple peaks in their P – V characteristics under partial shading conditions. Owing to the limitation in the abilities of conventional maximum power point tracking algorithms in such cases, the application of other optimisation algorithms has been explored. This study proposes a modified particle velocity‐based particle swarm optimisation (MPV‐PSO) algorithm for tracking the global power peak of the multiple peak P – V characteristics. The MPV‐PSO algorithm is both adaptive and deterministic in nature. It eliminates the inherent randomness in the conventional PSO algorithm by excluding the use of random numbers in the velocity equation. The proposed algorithm also eliminates the need for tuning the weight factor, the cognitive and social acceleration coefficients by introducing adaptive values for them which adjust themselves based on the particle position. These adaptive values also solve problems like oscillations about the global best position during steady‐state operation and particles getting trapped in local minima. The effectiveness of the proposed MPV‐PSO algorithm is validated through MATLAB/Simulink simulations and hardware experiments.