| Summary: | 碩士 === 國立暨南國際大學 === 電機工程學系 === 104 === The major target of this thesis is to develop the maximum power tracker of
photovoltaic (PV) systems under the partial-shading conditions. Since the weather is
unpredictable, there might exist local and global maximum power points (MPP) in the
systems. Therefore, we must be able to track the global MPP under the partial-shading
conditions in order to make our PV systems offer effective maximum power output for
obtaining optimal system performance.
First of all, the mathematical model is established for a PV array system to
investigate and analyze the voltage and power output under partial-shade and
non-partial-shade conditioning. However, the output power of PV systems could have
various MPP under partial-shading conditions, so we have to determine an appropriate
technology for the tracking control of global MPP.
A novel concept is presented to modify the traditional particle swarm optimization
method for strengthening algorithm capability and improving the system performance.
In addition to using linear decreasing inertia weight, we apply nonlinear adapting
learning factors for enhancing the tracking ability. It can avoid falling into local
maximum solutions and provide the system to have more accurate convergence. As a
result, the simulation results show that the modified particle swarm optimization has the
potentials to track the global MPP with accurate rate of convergence under
partial-shading conditions.
|
|---|
