Open AccessDesign and Simulation of Improved Soft Computing Based MPPT for Solar PV System Under Variable Irradiance Condition
Author(s) -
Ruchi Sharma Surendra Singh Dua
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i6.4363
Subject(s) - photovoltaic system , renewable energy , computer science , maximum power point tracking , automotive engineering , duty cycle , solar irradiance , block (permutation group theory) , simulation , environmental science , electrical engineering , inverter , engineering , voltage , meteorology , mathematics , physics , geometry
Renewable energy sources are becoming more common in the energy generation field these days. Renewable energy sources such as photovoltaic (PV) systems, wind power (WP), and biomass are gaining popularity due to their ease of use, low cost, and low environmental impact. The environmental issues, declining fuel supplies, and increasing energy demands have drawn our attention to the glimmer of hope for a future focused entirely on sustainable and non-polluting energy sources. Photovoltaic (PV) power generation is becoming more common in comparison to other renewable energy sources due to advantages such as ease of access, low cost, low environmental emissions, and lower maintenance costs. In this dissertation, three separate Maximum power point monitoring techniques are used to construct a solar PV system (MPPT). Modeling and simulation using the MATLAB Simulink programmeare being used to check the effectiveness of the proposed scheme. The model is investigated using two partial shading patterns. By providing different values of input radiations to all four modules connected in sequence, we were able to create partial shading conditions using the PV array block. The panel's output is fed to the optimization technique block, which then feeds the boost converter from their duty cycle output. Under partial shading, the results show that the Particle Swarm Optimization algorithm outperforms the Perturb and Observe and Incremental Conductance algorithms..