Open AccessPARTICLE SWARM OPTIMIZATION WITH INDIVIDUALLY BIASED PARTICLES FOR RELIABLE AND ROBUST MAXIMUM POWER POINT TRACKING UNDER PARTIAL SHADING CONDITIONS
Author(s) -
K. S. Baktybekov
Publication year - 2020
Publication title -
eurasian physical technical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.194
H-Index - 2
eISSN - 2413-2179
pISSN - 1811-1165
DOI - 10.31489/2020no2/128-137
Subject(s) - duty cycle , maximum power point tracking , benchmark (surveying) , particle swarm optimization , maximum power principle , photovoltaic system , power (physics) , computer science , control theory (sociology) , point (geometry) , shading , power optimizer , tracking (education) , operating point , task (project management) , mathematical optimization , algorithm , engineering , mathematics , electronic engineering , artificial intelligence , control (management) , electrical engineering , physics , pedagogy , computer graphics (images) , systems engineering , inverter , psychology , geometry , geodesy , quantum mechanics , geography
Efficient power control techniques are an integral part of photovoltaic system design. One of the means of managing power delivery is regulating the duty cycle of the DC to DC converter by various algorithms to operate only at points where power is maximum power point. Search has to be done as fast as possible to minimize power loss, especially under dynamically changing irradiance. The challenge of the task is the nonlinear behavior of the PV system under partial shading conditions. Depending on the size and structure of the photovoltaic panels, PSC creates an immense amount of possible P-V curves with numerous local maximums - requiring an intelligent algorithm for determining the optimal operating point. Existing benchmark maximum power point tracking algorithms cannot handle multiple peaks, and in this paper, we offer an adaptation of particle swarm optimization for the specific task.