Abstract
Maximum power point tracking technique for PV panels with support of online learning artificial neural network is offered. Mathematical model of the system is implemented in Matlab/Simulink environment. Maximum power point tracking is performed using IncCond algorithm and radial basis function artificial neural network. Several criteria for estimation of system performance were derived. It is shown that ANN can increase overall system efficiency by 10%.DOI: http://dx.doi.org/10.5755/j01.eee.18.10.3065
Highlights
Maximum power point tracking (MPPT) enables to increase efficiency of electricity production of photovoltaic (PV) module [1]
To reach the maximum instantaneous power the controller must adjust the load of PV module according MPPT algorithm depending on varying cloudiness and temperature of the module
This paper presents analysis of Incremental Conductance algorithm (IncCond) MPPT algorithm and comparison of operation with and without artificial neural networks (ANN)
Summary
In each curve an intersection point of maximum current I and voltage U can be found at witch solar m ax m ax module generates the maximum power Pm ax U m ax I m ax. The current-voltage characteristic is unique for each set of SEF and temperature values, so as the
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