Improving the efficiency of grid connected PV system for real operating conditions

Maria Carla Di Vincenzo

Research output: ThesisDoctoral Thesis

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This PhD thesis is focused on modelling and development of an improved
Maximum Power Point Tracking (MPPT) designed for real
operating conditions.
Real operating conditions involve changing irradiance and temperature
and also often partial shading of the array. It is also common
for there to be temperature variation across the array, and also some
dierences in the intrinsic quality and eciency of individual cells and
modules. These eects combine to give a degree of mismatch between
the cells and modules within the array that is time varying.
Commercial inverters are not designed to deal with the resulting nonideal
system IV curves, and thus can deliver poor MPPT performance
that can degrade signicantly the overall eciency of power conversion.
The novelty of this research is the development of a Maximum Power
Point Tracking algorithm able to indentify accurately and rapidly the
MPP under real operating conditions, and thus improve the system
performance especially when the mismatch issues outlined above lead
to multiple local maxima in the power output of the array (as a function
of array voltage).
To underpin the development of the new MPPT algorithm, a detailed
model of the PV system was developed. This is built up from models
of individual cells and modules so as to properly represent cell mismatch.
This model has been tested and validated using real measured
data from a test rig installed on the roof of James Weir Building of
Strathclyde University. The test rig was equipped with comprehensive
and appropriate instrumentation to measure both the ambient
conditions and the PV performance. Over an extended period of
monitoring a substantial amount of high quality detailed data was
collected from the roof test rig, and this has been used to develop and
rene an algorithm able to track the MPP highly eectively under
time varying real outdoor operating conditions.
The algorithm uses an Articial Neural Network (ANN) to predict
the MPP in the case of partial shading and also any other operating
conditions likely to be experienced; the algorithm includes additional
code to assist the ANN in tracking the true maximum within a variable
time step. It has been implemented on a modelled DC/DC converter
to test dierent power conditions and also dierent types of modules
with dierent Fill Factors.
Finally, the control technique developed has been implemented in a
real DC/DC converter but using an electronic PV array simulator
rather than the outdoor system to provide more controlled operational
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
Publication statusPublished - 2012


  • real operating conditions
  • improving efficiency
  • grid connected
  • PV system


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