Genetic algorithm-based arrangement of photovoltaic modules in arrays for mismatch loss mitigation
Photovoltaic (PV) arrays which are the main part of PV generators consist of PV modules and PV modules are composed of PV cells. Such a modular nature is a blessing when it provides flexibility in system sizing but is a curse when it combines with inequality of PV modules and causes the so-called m...
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| Format: | Thesis |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/57537/1/FK%202015%2054RR.pdf |
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| Summary: | Photovoltaic (PV) arrays which are the main part of PV generators consist of PV modules and PV modules are composed of PV cells. Such a modular nature is a
blessing when it provides flexibility in system sizing but is a curse when it combines with inequality of PV modules and causes the so-called mismatch power loss. Recent investigations have reported this type of mismatch losses from 1% to 10%. The conventional techniques to minimize mismatch losses are distributed maximum power
point tracking (MPPT), array reconfiguration and module sorting technique. Module sorting techniques which sort modules in arrays by one characteristic parameter are
superior among all conventional solutions. This study proposes a new method for arranging modules in arrays using a genetic algorithm (GA) to find the arrangement
with lowest mismatch losses and highest output power. Some data sets of modules are generated through a stochastic process and organized in different arrays. These data sets are used to carry out several simulations in MATLAB. These simulations cover calculation of mismatch losses at standard test circumstance (STC) and also energy yield under the GA based arrangement as well as the sorting techniques. Results prove that the proposed arrangement of modules in arrays reduces mismatch losses and subsequently improves the energy yield more effectively than what conventional sorting techniques do. The GA based arrangement is also validated with other data sets of modules that are collected from 3 practical arrays. Results of participation of these
practical data sets also support the superiority of the GA based arrangement over Morting techniques. Comparison between best sorting technique and the GA based
arrangement turned out that the GA based arrangement recovers daily energy ranging from 50 Wh to 320 Wh and monthly energy ranging from 1010 Wh to 6240 Wh
depending on the array size. Similar comparison for the practical arrays shows an annual recoverable energy ranging from 6.35 kWh to 11.8 kWh and up to 278 kWh for
an array with defective modules. |
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