156. POWER OPTIMIZATION FOR PHOTOVOLTAIC MICRO-CONVERTERS USING MULTIVARIABLE GRADIENT-BASED EXTREMUM-SEEKING
Department: Mechanical & Aerospace Engineering
Research Institute Affiliation: Center for Control Systems and Dynamics (CCSD)
Faculty Advisor(s):
Miroslav Krstic | Sridhar Seshagiri
Primary Student
Name: Azad Ghaffari
Email: aghaffar@ucsd.edu
Phone: 858-886-6486
Grad Year: 2013
Abstract
It is well-known that distributed architectures such
as micro-converters and micro-inverters for photovoltaic (PV)
systems can recover between 10%-30% of annual performance
loss or more that is caused by partial shading and/or module
mismatch. In this work, we present a novel multivariable
gradient-based extremum-seeking (ES) design to extract maximum
power from an arbitrary micro-converter configuration
of PV modules, that includes cascade and parallel connections.
Conventional maximum power point tracking (MPPT) schemes
for micro-converters (where each PV module is coupled to
its own DC-DC converter) employ a decentralized control,
with one peak seeking scheme per each PV module, thereby
requiring one control loop and two sensors per module (one
each for current and voltage). By contrast, the scheme that we
present employs a single control loop with just two sensors,
one for the overall array output current and the other one
for the DC bus voltage. This centralized design provides
more flexibility in tuning the parameters of the controller,
and also takes into account interactions between PV modules.
The computational effort of our design is not higher than
that of the conventional scheme, and simulation results using
Simulink?s SimPowerSystems toolbox show that our proposed
design outperforms the conventional one. Thus, our proposed
design offers two benefits: (i) the balance-of-system (BOS)
cost reduction as a result of the significantly lower number
of sensors, and (ii) improved performance, both contributing
towards reduced average cost/watt, and enhancing the economic
viability of solar.
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